1,941 research outputs found
SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequences to Navigate Towards a Rewarded Goal
How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.Riverside Reserach Institute; Defense Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225); National Science Foundation (IRI 90-24877, SBE-0345378); Office of Naval Research (N00014-92-J-1309, N00014-91-J-4100, N00014-01-1-0624, N00014-01-1-0624); Pacific Sierra Research (PSR 91-6075-2
From clothing to identity; manual and automatic soft biometrics
Soft biometrics have increasingly attracted research interest and are often considered as major cues for identity, especially in the absence of valid traditional biometrics, as in surveillance. In everyday life, several incidents and forensic scenarios highlight the usefulness and capability of identity information that can be deduced from clothing. Semantic clothing attributes have recently been introduced as a new form of soft biometrics. Although clothing traits can be naturally described and compared by humans for operable and successful use, it is desirable to exploit computer-vision to enrich clothing descriptions with more objective and discriminative information. This allows automatic extraction and semantic description and comparison of visually detectable clothing traits in a manner similar to recognition by eyewitness statements. This study proposes a novel set of soft clothing attributes, described using small groups of high-level semantic labels, and automatically extracted using computer-vision techniques. In this way we can explore the capability of human attributes vis-a-vis those which are inferred automatically by computer-vision. Categorical and comparative soft clothing traits are derived and used for identification/re identification either to supplement soft body traits or to be used alone. The automatically- and manually-derived soft clothing biometrics are employed in challenging invariant person retrieval. The experimental results highlight promising potential for use in various applications
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Improving Human Face Recognition Using Deep Learning Based Image Registration And Multi-Classifier Approaches
Face detection, registration, and recognition have become a fascinating field for researchers. The motivation behind the enormous interest in the topic is the need to improve the accuracy of many real-time applications. Countless methodologies have been acknowledged and presented in the past years. The complexity of the human face visual and the significant changes based on different effects make it more challenging to design as well as implementing a powerful computational system for object recognition in addition to human face recognition. Using supervised learning often requires extensive training for the computer which results in high execution times. It is an essential step in the face recognition to apply strong preprocessing approaches such as face registration to achieve a high recognition accuracy rate. Although there are exist approaches do both detection and recognition, we believe the absence of a complete end-to-end system capable of performing recognition from an arbitrary scene is in large part due to the difficulty in alignment. Often, the face registration is ignored, with the assumption that the detector will perform a rough alignment, leading to suboptimal recognition performance. In this research, we presented an enhanced approach to improve human face recognition using a back-propagation neural network (BPNN) and features extraction based on the correlation between the training images. A key contribution of this paper is the generation of a new set called the T-Dataset from the original training data set, which is used to train the BPNN. We generated the T-Dataset using the correlation between the training images without using a common technique of image density. The correlated T-Dataset provides a high distinction layer between the training images, which helps the BPNN to converge faster and achieve better accuracy. Data and features reduction is essential in the face recognition process, and researchers have recently focused on the modern neural network. Therefore, we used using a classical conventional Principal Component Analysis (PCA) and Local Binary Patterns (LBP) to prove that there is a potential improvement even using traditional methods. We applied five distance measurement algorithms and then combined them to obtain the T-Dataset, which we fed into the BPNN. We achieved higher face recognition accuracy with less computational cost compared with the current approach by using reduced image features. We test the proposed framework on two small data sets, the YALE and AT&T data sets, as the ground truth. We achieved tremendous accuracy. Furthermore, we evaluate our method on one of the state-of-the-art benchmark data sets, Labeled Faces in the Wild (LFW), where we produce a competitive face recognition performance. In addition, we presented an enhanced framework to improve the face registration using deep learning model. We used deep architectures such as VGG16 and VGG19 to train our method. We trained our model to learn the transformation parameters (Rotation, scaling, and shifting). By leaning the transformation parameters, we will able to transfer the image back to the frontal domain. We used the LFW dataset to evaluate our method, and we achieve high accuracy
A Self-Organizing Neural System for Learning to Recognize Textured Scenes
A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices interact with the hippocampal system to learn visual recognition categories and their names. FACADE processing generates a vector of boundary and surface properties, notably texture and brightness properties, by utilizing multi-scale filtering, competition, and diffusive filling-in. Its context-sensitive local measures of textured scenes can be used to recognize scenic properties that gradually change across space, as well a.s abrupt texture boundaries. ART incrementally learns recognition categories that classify FACADE output vectors, class names of these categories, and their probabilities. Top-down expectations within ART encode learned prototypes that pay attention to expected visual features. When novel visual information creates a poor match with the best existing category prototype, a memory search selects a new category with which classify the novel data. ARTEX is compared with psychophysical data, and is benchmarked on classification of natural textures and synthetic aperture radar images. It outperforms state-of-the-art systems that use rule-based, backpropagation, and K-nearest neighbor classifiers.Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657
Multiclass Object Detection with Single Query in Hyperspectral Imagery Using Class-Associative Spectral Fringe-Adjusted Joint Transform Correlation
We present a deterministic object detection algorithm capable of detecting multiclass objects in hyperspectral imagery (HSI) without any training or preprocessing. The proposed method, which is named class-associative spectral fringe-adjusted joint transform correlation (CSFJTC), is based on joint transform correlation (JTC) between object and nonobject spectral signatures to search for a similar match, which only requires one query (training-free) from the object\u27s spectral signature. Our method utilizes class-associative filtering, modified Fourier plane image subtraction, and fringe-adjusted JTC techniques in spectral correlation domain to perform the object detection task.
The output of CSFJTC yields a pair of sharp correlation peaks for a matched target and negligible or no correlation peaks for a mismatch. Experimental results, in terms of receiver operating characteristic (ROC) curves and area-under-ROC (AUROC), on three popular real-world hyperspectral data sets demonstrate the superiority of the proposed CSFJTC technique over other well-known hyperspectral object detection approaches
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