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While everyone is super hyped about generative AI, computer vision researchers have been working in the background on significant advancements in deep learning architectures. YOLOv9 was just released with some noteworthy advancements relevant to parameter efficient models. In this episode, Chris and Daniel dig into the details and also discuss advancements in parameter efficient LLMs, such as Microsofts 1-Bit LLMs and Qualcomm's new AI Hub.

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Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation.

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.

Abstract: An in situ computer vision method is presented for omnidirectional bee counting on landing pads of Langstroth beehives used by many apiarists worldwide. Bee counts are computed from static images of beehive landing pads. The presented method automates landing pad localization and improves bee counts through elimination of landing pad skew. Two bee counting algorithms are presented for counting bees on localized landing pads. The first bee counting algorithm is based on the 1D Haar Wavelet Transform. The second algorithm is based on contour analysis. The relative performance of both algorithms is evaluated on a sample of 793 images obtained from two electronic beehive monitoring devices deployed in live Langstorth beehives in northern Utah. http://www.icgst.com/paper.aspx?pid=P1151712556

Abstract: An in situ computer vision method is presented for omnidirectional bee counting on landing pads of Langstroth beehives used by many apiarists worldwide. Bee counts are computed from static images of beehive landing pads. The presented method automates landing pad localization and improves bee counts through elimination of landing pad skew. Two bee counting algorithms are presented for counting bees on localized landing pads. The first bee counting algorithm is based on the 1D Haar Wavelet Transform. The second algorithm is based on contour analysis. The relative performance of both algorithms is evaluated on a sample of 793 images obtained from two electronic beehive monitoring devices deployed in live Langstorth beehives in northern Utah. http://www.icgst.com/paper.aspx?pid=P1151712556

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In recent times, there has been an increase in the stability and integration of augmented reality (AR) technology in everyday applications. AR relies on tracking techniques to capture the characteristics of the surrounding environment. Tracking falls into two categories: outdoor and indoor. While outdoor tracking predominantly relies on the global positioning system (GPS), it is performance indoors is hindered by imprecise GPS signals. Indoor tracking offers a solution for navigating complex indoor environments. This paper introduces an indoor tracking system that combines smartphone sensor data and computer vision using the oriented features from accelerated and segments test and rotated binary robust independent elementary features (ORB) algorithm for feature extraction, along with brute force match (BFM) and k-nearest neighbor (KNN) for matching. This approach outperforms previous systems, offering efficient navigation without relying on pre-existing maps. The system uses the A* algorithm to find the shortest path and cloud computing for data storage. Experimental results demonstrate an impressive 99% average accuracy within a 7-10 cm error range, even in scenarios with varying distances. Moreover, all users successfully reached their destinations during the experiments. This innovative model presents a promising advancement in indoor tracking, enhancing the accuracy and effectiveness of navigation in complex indoor spaces.

In recent times, there has been an increase in the stability and integration of augmented reality (AR) technology in everyday applications. AR relies on tracking techniques to capture the characteristics of the surrounding environment. Tracking falls into two categories: outdoor and indoor. While outdoor tracking predominantly relies on the global positioning system (GPS), it is performance indoors is hindered by imprecise GPS signals. Indoor tracking offers a solution for navigating complex indoor environments. This paper introduces an indoor tracking system that combines smartphone sensor data and computer vision using the oriented features from accelerated and segments test and rotated binary robust independent elementary features (ORB) algorithm for feature extraction, along with brute force match (BFM) and k-nearest neighbor (KNN) for matching. This approach outperforms previous systems, offering efficient navigation without relying on pre-existing maps. The system uses the A* algorithm to find the shortest path and cloud computing for data storage. Experimental results demonstrate an impressive 99% average accuracy within a 7-10 cm error range, even in scenarios with varying distances. Moreover, all users successfully reached their destinations during the experiments. This innovative model presents a promising advancement in indoor tracking, enhancing the accuracy and effectiveness of navigation in complex indoor spaces.

Agricultural sector is one of the economic pillars of developing nations, because it provides means of boosting gross domestic profit. However, weeds pose a threat to food crop by competing with it for nutrients and undermining the profit to be made from it. The treatment of these weeds is necessary, but at minimal impact on the actual food crop. Herbicide usage is one major means of weed control, owning to the expensive and labour-intensive nature of hand weeding. Recently, the need for site specific spraying has been on the rise because of health concerns which have been raised on the effect of herbicides on food crops and the effect on the environment. Most research on the field focuses on accurately identifying the weeds whilst neglecting the weed control. In this research, we apply fuzzy logic-based expert system to control how herbicide is sprayed on low-land rice in order to reduce excessive herbicide usage. The system supplies the control with weed density (Box size) and confidence level. The values of both are then passed to the fuzzy logic control for spray decision. The Sugeno as well as Mamdani models were tested using generated values for detected weed box size and confidence levels of the computer vision. The mean absolute error obtained was 0.9 for both, and 0.3 and 0.2 respectively, for the mean square error. The error shows how accurate the system can be and with low error value, it shows that the system implementation is capable of providing control for spraying of herbicides which in turn will yield more returns for low-land rice farmers.

In this study, a smart attendance system was created using computer vision techniques embedded in the Raspberry Pi device. The initial process is carried out by recording students taking certain courses and taking facial images for the needs of the system database. In the next stage, the system will be regulated according to the time of lecture entry to determine which students will attend the lecture. Every student who wants to enter the classroom is identified by taking facial images with a camera from the Raspberry Pi device to identify and determine the time students enter to attend lectures. Each image taken will be processed to detect the presence of a face using the ViolaJones method and to extract features using the LBP method to obtain the feature value of each image. The results obtained will be stored in the system for the facial recognition process. The final stage of the system being built is to perform face recognition according to the initial image to carry out the attendance process. This process will be carried out using the normalized cross correlation (NCC) technique, in which the highest feature similarity obtained between the initial image and the newly captured image is the result of recognition by the system. From the trials that have been carried out, the developed system gives good results in obtaining attendance management in a fairly efficient manner, and the algorithm proposed for facial recognition obtains good results with an accuracy rate of 97.54%

The image datasets that are most widely used for training deep learning models are specifically developed for applications. This study introduces a novel dataset aimed at augmenting the existing data for the identification of figs in their natural habitats, specifically in the wilderness. In the present study, researchers have generated numerous image datasets specifically for object detection focus on applications in agriculture. Regrettably, it is exceedingly difficult for us to obtain a specialized dataset specifically designed for detecting figs. To tackle this issue, a grand total of 462 photographs of fig fruits were gathered. The augmentation technique was utilized to substantially increase the size of the dataset. Ultimately, we conduct an examination of the dataset by doing a baseline performance study for bounding-box detection using established object detection methods, specifically you only look once (YOLO) version 3 and YOLOv4. The performance obtained on the test photos of our dataset is satisfactory. For farmers, the capacity to identify and oversee fig fruits in their natural or developed environments can be highly advantageous. The detecting device offers instantaneous data regarding the quantity of mature figs, facilitating decision-making procedures.

Talk given by Tom Dean, of Google. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley on January 20, 2010.. Abstract. Graphics processors (GPUs) and massively-multi-core architectures are becoming more powerful, less costly and more energy efficient, and the related programming language issues are beginning to sort themselves out. That said most researchers don’t want to be writing code that depends on any particular architecture or parallel programming model. Linear algebra, Fourier analysis and image processing have standard libraries that are being ported to exploit SIMD parallelism in GPUs. We can depend on the massively-multiple-core machines du jour to support these libraries and on the high-performance-computing (HPC) community to do the porting for us or with us. These libraries can significantly accelerate important applications in image processing, data analysis and information retrieval. We can develop APIs and the necessary run-time support so that code relying on these libraries will run on any machine in a cluster of computers but exploit GPUs whenever available. This strategy allows us to move toward hybrid computing models that enable a wider range of opportunities for parallelism without requiring the special training of programmers or the disadvantages of developing code that depends on specialized hardware or programming models. This talk summarizes the state of the art in massively-multi-core architectures, presents experimental results that demonstrate the potential for significant performance gains in the two general areas of image processing and machine learning, provides examples of the proposed programming interface, and some more detailed experimental results on one particular problem involving video-content analysis.

The methods of depth determination used in scene analysis are discussed. Previous schemes incorporating a single view of the scene are reviewed. These include methods requiring a special illumination source. A review of the work using two (stereoscopic) images is presented. Finally, a method for extracting objects from a pair of run length coded images is developed. The procedure relies on feature extraction and correllation techniques developed specifically for operation on objects in run length coded images. (Author)

Computer Vision Brown C. M

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This collection of elementary facts and definitions is meant to be a guide to concepts and results of color vision and color science research that are likely to be of interest to computer visionaries. There are a few thoughts about research topics here, but no results. (Author)

The development of computer vision and the identification of objects in an image is important for many areas of scientific, medical, and commercial ventures. The available literature details many experiments and algorithms in these fields. In this study, the possibility of identifying road surfaces in aerial black and white photographs using texture analysis is examined. A system using texture that was successful in identifying road surfaces is presented as part of this research.

The method has been developed that can take a human description of an object's spatial appearance and produce a PROLOG representation. The object's appearance is currently in terms of an edge map and the English descriptions are stylised accounts of the salient features and combinations of features found in this representation. At present the translation is performed by hand. However, suggestions are made on how this process can be automated. A prototype translator has been implemented. The PROLOG model is expressed as a hierarchy about the object's appearance, terminating in plausible low-level image primitives. A way is proposed of matching the hierarchy against an image for object recognition in isolation from its background. This reduces the search space of features and feature combinations that the matcher has to consider, so avoiding some of the combinations that the matcher has to consider, so avoiding some of the combinatorial problems when using PROLOG. Extensions using fuzzy logic to deal with uncertain image data and the vagueness of natural language are discussed.

This report addresses FLIR processing, LADAR processing and electronic terrain board modeling. In our discussion on FLIR processing, we have analyzed the issues of classifiability of FLIR features, computationally efficient algorithms for target segmentation, metrics, etc. The discussion on LADAR includes a comparison of a number of different approaches to the segmentation of target surfaces from range images, extraction of silhouettes at different ranges, and reasoning strategies for the recognition of targets and estimation of their aspects. Regarding electronic terrain board modeling, we have shown how the readily available wire-frame data for strategic targets can be converted into volumetric models utilizing the concepts of constructive solid geometry; we then show how from the resulting volumetric models it is possible to generate synthetic range images that are very similar to real LADAR images. We also show how sensor noise can be added to these synthetic images to make them even more realistic. Keywords: Vision; Pattern recognition; Audio visual system; Robotics; Artificial intelligence.

The VEIL (Vision Environment Integrating Loom) project integrated advanced Knowledge Representation (KR) technology (Loom) with image understanding technology. The major innovations are as follows: 1) Use of declarative knowledge (as opposed to procedural knowledge) in vision systems made extending the recognition capabilities of the software easier. 2) Facilitated integrating high-level vision routines (recognizing sequences of scenes) with low-level routines that recognize picture elements. 3) Declarative knowledge enabled interaction with the system at a level of abstraction appropriate to the domain task. This included associating collateral information with the objects recognized by low-level image understanding programs. Loom is a powerful tool incorporating strong, frame-based representation capabilities, explicit term subsumption, and several powerful reasoning paradigms (including logical deduction, object-oriented methods, and production rules). Loom also provides knowledge representation integrity through consistency checking and truth maintenance. Infusing these facilities into the vision problem area, where strong KR capabilities have not yet been developed, significantly altered and improved the methodology for constructing vision systems. Finally, Loom was interfaced to a variety of vision processing elements to provide a new tool of extended capabilities. The result is a powerful software environment for the development of vision systems.

The VEIL (Vision Environment Integrating Loom) project integrated advanced Knowledge Representation (KR) technology (Loom) with image understanding technology. The major innovations are as follows: 1) Use of declarative knowledge (as opposed to procedural knowledge) in vision systems made extending the recognition capabilities of the software easier. 2) Facilitated integrating high-level vision routines (recognizing sequences of scenes) with low-level routines that recognize picture elements. 3) Declarative knowledge enabled interaction with the system at a level of abstraction appropriate to the domain task. This included associating collateral information with the objects recognized by low-level image understanding programs. Loom is a powerful tool incorporating strong, frame-based representation capabilities, explicit term subsumption, and several powerful reasoning paradigms (including logical deduction, object-oriented methods, and production rules). Loom also provides knowledge representation integrity through consistency checking and truth maintenance. Infusing these facilities into the vision problem area, where strong KR capabilities have not yet been developed, significantly altered and improved the methodology for constructing vision systems. Finally, Loom was interfaced to a variety of vision processing elements to provide a new tool of extended capabilities. The result is a powerful software environment for the development of vision systems.

The VEIL (Vision Environment Integrating Loom) project integrated advanced Knowledge Representation (KR) technology (Loom) with image understanding technology. The major innovations are as follows: 1) Use of declarative knowledge (as opposed to procedural knowledge) in vision systems made extending the recognition capabilities of the software easier. 2) Facilitated integrating high-level vision routines (recognizing sequences of scenes) with low-level routines that recognize picture elements. 3) Declarative knowledge enabled interaction with the system at a level of abstraction appropriate to the domain task. This included associating collateral information with the objects recognized by low-level image understanding programs. Loom is a powerful tool incorporating strong, frame-based representation capabilities, explicit term subsumption, and several powerful reasoning paradigms (including logical deduction, object-oriented methods, and production rules). Loom also provides knowledge representation integrity through consistency checking and truth maintenance. Infusing these facilities into the vision problem area, where strong KR capabilities have not yet been developed, significantly altered and improved the methodology for constructing vision systems. Finally, Loom was interfaced to a variety of vision processing elements to provide a new tool of extended capabilities. The result is a powerful software environment for the development of vision systems.

The VEIL (Vision Environment Integrating Loom) project integrated advanced Knowledge Representation (KR) technology (Loom) with image understanding technology. The major innovations are as follows: 1) Use of declarative knowledge (as opposed to procedural knowledge) in vision systems made extending the recognition capabilities of the software easier. 2) Facilitated integrating high-level vision routines (recognizing sequences of scenes) with low-level routines that recognize picture elements. 3) Declarative knowledge enabled interaction with the system at a level of abstraction appropriate to the domain task. This included associating collateral information with the objects recognized by low-level image understanding programs. Loom is a powerful tool incorporating strong, frame-based representation capabilities, explicit term subsumption, and several powerful reasoning paradigms (including logical deduction, object-oriented methods, and production rules). Loom also provides knowledge representation integrity through consistency checking and truth maintenance. Infusing these facilities into the vision problem area, where strong KR capabilities have not yet been developed, significantly altered and improved the methodology for constructing vision systems. Finally, Loom was interfaced to a variety of vision processing elements to provide a new tool of extended capabilities. The result is a powerful software environment for the development of vision systems.

Humans communicate with one another using body language (gestures), such as hand and head gestures, facial expressions, lip movements, and so forth, or through natural language channels like words and writing. Sign language comprehension is just as crucial as knowing normal language. The primary means of communication for those who are hard of hearing is sign language. Without a translation, speaking with other hearing people can be difficult for those with hearing impairments. Because of this, the social lives of deaf people would be greatly improved by the installation of a system that recognizes sign language. In order to recognize the features of the hand in pictures captured by a webcam, we have presented in this study a marker-free, visual American Sign Language recognition system that makes use of image processing, computer vision, and neural network techniques. This paper deals with full phrase gestures that are used regularly every day and methods used to converted them to text. A number of image processing techniques have been used to identify the hand shape from continuous pictures. The Haar Cascade Classifier is used to determine the interpretation of signs and their associated meaning.

Trimester 1 2015 / 2016

In this paper we broadly consider techniques which utilize projections on rays for data collection, with particular emphasis on optical techniques. We formulate a variety of imaging techniques as either special cases or extensions of tomographic reconstruction. We then consider how the techniques must be extended to describe objects containing occlusion, as with a self-occluding opaque object. We formulate the reconstruction problem as a regularized nonlinear optimization problem to simultaneously solve for object brightness and attenuation, where the attenuation can become infinite. We demonstrate various simulated examples for imaging opaque objects, including sparse point sources, a conventional multiview reconstruction technique, and a super-resolving technique which exploits occlusion to resolve an image.

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Employing the allegorical imagery from the film "The Matrix", we motivate and discuss our `Cyborg Astrobiologist' research program. In this research program, we are using a wearable computer and video camcorder in order to test and train a computer-vision system to be a field-geologist and field-astrobiologist.

Employing the allegorical imagery from the film "The Matrix", we motivate and discuss our `Cyborg Astrobiologist' research program. In this research program, we are using a wearable computer and video camcorder in order to test and train a computer-vision system to be a field-geologist and field-astrobiologist.

The energy expended to cool the occupied areas by air conditioners represents a substantial share of the total energy exhausted in buildings. Therefore, developing strategies to reduc e this energy is crucial. One of the preponderance strategies adopted to depreciate energy consumption in buildings is the occupancy - based strategy. In this research, an innovative model was established to achieve the goal of reducing cooling energy consum ed in buildings based on occupancy - based combined with a constant temperature setpoint strategy in two phases, and each phase engrosses in 20 days. Phase one is to identify the extent of cooling energy employed according to the use of room occupants and it s costs in consumption was 276.01 kWh after completion of this phase. Sequentially, constructing phase two intended to reduce cooling energy consumption by employing an automatic air - conditioner ( AC ) control strategy relying on an improved human detection algorithm with a 25 °C as temperature setpoint, resulting in 112.45 kWh of consumption. To complement the motives for elaboration, the human detection measurement using you only look once ( YOLO ) improved by applying pre - processing algorithms to reach an average human detection enhancement of 21.2%. The proposed model results showed that potential savings associated with the embraced strategy decreases by more than anticipated as the amount of red uced energy reached 59% savings.

It has been argued that knowledge-based systems (KBS) must reason from evidential information - i.e., information that is to some degree uncertain, imprecise, and occasionally inaccurate. This is no less true of KBS that operate in the domain of computer-based image interpretation. Recent research has suggested that the work of Dempster and Shafer (DS) provides a viable alternative to Bayesian-based techniques for reasoning from evidential information. In this paper, we discuss some of the differences between the DS theory and some popular Bayesian-based approaches to effecting the reasoning task. We then discuss some work on integrating the DS theory into a knowledge-based high-level computer vision system in order to examine various aspects of this new technology that have not been explored to date. Results from a large number of image interpretation experiments will be presented. These results suggest that a KBS's performance improves substantially when it exploits various features of the DS theory that are not readily available in pure Bayesian-based approaches.

Computer Vision By 5 Minutes Engineering

Computer vision and image processing are techniques which are generally deployed to utilize the video feed or images/video frames to analyze and take steps to prevent or solve problem scenarios

Computer vision and image processing are techniques which are generally deployed to utilize the video feed or images/video frames to analyze and take steps to prevent or solve problem scenarios

70% of us have got Computer Vision Syndrome...

The ability to recognize the shape and movement of hands can help improve the user experience in a wide range of technical domains and platforms. It can help you understand sign language and move your hands in the right way, for example. It can also make it possible for digital information and materials to be added on top of the real world in augmented reality. Here, I talk about a real-time, on-device hand gesture recognition solution that lets us control our system’s graphical user interface (GUI) with static and dynamic hand gestures that can be trained to do a set of actions that are similar to what we do with our mouse and keyboard. It is built with MediaPipe-Hands, which finds the palm landmark point. The data is then sent through a pipeline of data-preprocessing functions and trained with two models: one for static gesture recognition and one for dynamic gesture recognition. In real-time, the models are then used to detect similar gestures on-device from a video-capturing device like a webcam.

Abstract — The usage of computer in day to day affairs is a basic need of everyone. No individual or organization can work without computer in the present era. However, extravagant practice of involving computer technology demands certain degree of responsibility on the part of user to avoid or minimize associated harmful impacts that are badly affecting environment. Information and communications technology (ICT) infrastructure accounts for seemingly significant electricity usage and considered accountable for greenhouse gases (GHGs) globally. The overwhelming and improper usage of ICT is leading to continual increase in carbon foot printing and GHGs. Green computing is emerging as a prompting solution to this crisis. Foremost measure in this regard is to develop computing technology that cut down usage of power input thus may leads to significant reduction in CO 2 emission. Various proposed measures taken in this regard can be considered as effective approach to protect our environment from the hazardous material and its effects specially computers and related devices. We also highlighted computing related distresses, possible steps for its minimization through Green computing. The article also covers prospective measures ought to be taken to reduce the associated harmful impacts on our environment thereby protect planet from any future disaster.

Vision Spreadsheet: An Environment for Computer Vision Scott Determan If you have questions, email [email protected]

The performance and serviceability of asphalt pavements have a direct influence on people's daily lives. Timely detection of pavement cracks is crucial in the task of periodic pavement survey. This paper proposes and verifies a novel computer vision-based method for recognizing pavement crack patterns. Image processing techniques, including Gaussian steerable filters, projection integrals, and image texture analyses, are employed to characterize the surface condition of asphalt pavement roads. Light Gradient Boosting Machine, Deep Neural Network, and Convolutional Neural Network are employed to recognize various patterns including longitudinal, transverse, diagonal, minor fatigue, and severe fatigue cracks. A dataset, including 12,000 samples, has been collected to construct and verify the computer vision-based approaches. Based on experiments, it can be found that all three machine learning models are capable of delivering good categorization results with an accuracy rate > 0.93 and Cohen's Kappa coefficient > 0.76. Notably, the Light Gradient Boosting Machine has achieved the most desired performance with an accuracy rate > 0.96 and Cohen's Kappa coefficient > 0.88.

The first part of the talk will present and overview of our machine learning endeavors within the scope of Computational Pathology: (i) generating a gold standard based on clinical labeling experiments, (ii) using off-line and on-line ensemble learning techniques to train object detectors for cell nuclei and (iii) employing Bayesian survival statistics for biomarker detection and diagnosing cancer patients. Based on several interdisciplinary research projects I will demonstrate how insights gained from statistical modeling can be translated to biomedical knowledge and in which ways clinical decision making can benefit from it. In the second part of the talk I am going to give an outlook on learning under labeling uncertainty: In a large number of real world application an objective ground truth is not available or too expensive to acquire. In practice, as a last resort, one would ask several domain experts for their opinion about each object in question to generate a gold standard. Depending on the difficulty of the task this often results in ambiguous labeling due to disagreement between experts. The resulting labeling matrix poses a non-trivial challenge for supervised learning. We will investigate under which condition it is possible to learn more about the data generating distribution than just using majority vote. A positive result would have immense influence in domains where specific models can be trained for years by a large number of experts, e.g. medical decision support. I will illustrate the problem with examples from medicine and space exploration, i.e. the classification of cell nuclei and the recognition of volcanoes on Venus. ©2010 Microsoft Corporation. All rights reserved.

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Naval aviators who employ night vision goggles (NVG) face additional risks during nighttime operations. In an effort to reduce these risks, increased training with NVGs is suggested. Our goal was to design a computer-based, interactive multimedia system that would assist in the training of pilots who use NVGs. This thesis details the methods and techniques used in the development of the NVG multimedia prototype. It describes which hardware components and software applications were utilized as well as how the prototype was developed. Several facets of multimedia technology (sound, animation, video and three dimensional graphics) have been incorporated into the interactive prototype. For a more robust successive prototype, recommendations are submitted for future enhancements that include alternative methodologies as well as expanded interactions. Multimedia, Computer aided instruction, Night vision goggles.

The UCP change that assigned responsibility for CND to U.S. Space Command (USSC) is a fundamentally flawed attempt to correct a perceived deficiency in Information Operations (IO) doctrine and organizational design. The CND mission has been defined too narrowly and rigidly and will result in the introduction of exploitable vulnerabilities in the Defense Information Infrastructure (DII) These DII vulnerabilities will be easily and quickly exploited by sophisticated adversaries in a manner that is imperceptible to those charged with protecting it. USSC will not and cannot be effective in improving the integration of CND into military planning and operations. On the contrary, it will likely slow development and fielding of new defensive capabilities, unnecessarily complicate inter-service and inter-agency coordination, and potentially weaken the U.S. military's information security posture.

The UCP change that assigned responsibility for CND to U.S. Space Command (USSC) is a fundamentally flawed attempt to correct a perceived deficiency in Information Operations (IO) doctrine and organizational design. The CND mission has been defined too narrowly and rigidly and will result in the introduction of exploitable vulnerabilities in the Defense Information Infrastructure (DII) These DII vulnerabilities will be easily and quickly exploited by sophisticated adversaries in a manner that is imperceptible to those charged with protecting it. USSC will not and cannot be effective in improving the integration of CND into military planning and operations. On the contrary, it will likely slow development and fielding of new defensive capabilities, unnecessarily complicate inter-service and inter-agency coordination, and potentially weaken the U.S. military's information security posture.

Second in what's known as the "Dream Machine" trilogy on Laserdisc. This is the Pacific Arts Video Version

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An algorithm and software to implement the algorithm are being developed as means to estimate the state (that is, the position and velocity) of an autonomous vehicle, relative to a visible nearby target object, to provide guidance for maneuvering the vehicle. In the original intended application, the autonomous vehicle would be a spacecraft and the nearby object would be a small astronomical body (typically, a comet or asteroid) to be explored by the spacecraft. The algorithm could also be used on Earth in analogous applications -- for example, for guiding underwater robots near such objects of interest as sunken ships, mineral deposits, or submerged mines. It is assumed that the robot would be equipped with a vision system that would include one or more electronic cameras, image-digitizing circuitry, and an imagedata- processing computer that would generate feature-recognition data products.

US Geographic Combatant Commands (GCC's) are unprepared to effectively plan computer network operations (CNO) and incorporate them into military operations. This condition is not due to any failure of GCC commanders to recognize their warfighting responsibility. Current legal authorities and national policy primarily enable CNO support at the strategic level of war. However, they marginalize GCC CNO planning efforts by denying commanders CNO decision-making authority in the more decisive operational cyberwar. This paper will discuss the efficacy of this current approach to CNO within a framework of its missing component: a Department of Defense (DoD) strategic vision for how to use CNO to help win wars in the cyberspace domain.