Object Tracking Using Local Binary Descriptors

Visual tracking has become an increasingly important topic of research in the field of Computer Vision (CV). There are currently many tracking methods based on the Detect-then-Track paradigm. This type of approach may allow for a system to track a random object with just one initialization phase, but may often rely on constructing models to follow the object. Another limitation of these methods is that they are computationally and memory intensive, which hinders their application to resource constrained platforms such as mobile devices. Under these conditions, the implementation of Augmented Reality (AR) or complex multi-part systems is not possible. In this thesis, we explore a variety of interest point descriptors for generic object tracking. The SIFT descriptor is considered a benchmark and will be compared with binary descriptors such as BRIEF, ORB, BRISK, and FREAK. The accuracy of these descriptors is benchmarked against the ground truth of the object\u27s location. We use dictionaries of descriptors to track regions with small error under variations due to occlusions, illumination changes, scaling, and rotation. This is accomplished by using Dense-to-Sparse Search Pattern, Locality Constraints, and Scale Adaptation. A benchmarking system is created to test the descriptors\u27 accuracy, speed, robustness, and distinctness. This data offers a comparison of the tracking system to current state of the art systems such as Multiple Instance Learning Tracker (MILTrack), Tracker Learned Detection (TLD), and Continuously Adaptive MeanShift (CAMSHIFT)

Multivoxel codes for representing and integrating acoustic features in human cortex

Using fMRI and multivariate pattern analysis, we determined whether acoustic features are represented by independent or integrated neural codes in human cortex. Male and female listeners heard band-pass noise varying simultaneously in spectral (frequency) and temporal (amplitude-modulation [AM] rate) features. In the superior temporal plane, changes in multivoxel activity due to frequency were largely invariant with respect to AM rate (and vice versa), consistent with an independent representation. In contrast, in posterior parietal cortex, neural representation was exclusively integrated and tuned to specific conjunctions of frequency and AM features. Direct between-region comparisons show that whereas independent coding of frequency and AM weakened with increasing levels of the hierarchy, integrated coding strengthened at the transition between non-core and parietal cortex. Our findings support the notion that primary auditory cortex can represent component acoustic features in an independent fashion and suggest a role for parietal cortex in feature integration and the structuring of acoustic input. Significance statement A major goal for neuroscience is discovering the sensory features to which the brain is tuned and how those features are integrated into cohesive perception. We used whole-brain human fMRI and a statistical modeling approach to quantify the extent to which sound features are represented separately or in an integrated fashion in cortical activity patterns. We show that frequency and AM rate, two acoustic features that are fundamental to characterizing biological important sounds such as speech, are represented separately in primary auditory cortex but in an integrated fashion in parietal cortex. These findings suggest that representations in primary auditory cortex can be simpler than previously thought and also implicate a role for parietal cortex in integrating features for coherent perception

Salient Object Detection Techniques in Computer Vision-A Survey.

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end

Somatic ABC's: A Theoretical Framework for Designing, Developing and Evaluating the Building Blocks of Touch-Based Information Delivery

abstract: Situations of sensory overload are steadily becoming more frequent as the ubiquity of technology approaches reality--particularly with the advent of socio-communicative smartphone applications, and pervasive, high speed wireless networks. Although the ease of accessing information has improved our communication effectiveness and efficiency, our visual and auditory modalities--those modalities that today's computerized devices and displays largely engage--have become overloaded, creating possibilities for distractions, delays and high cognitive load; which in turn can lead to a loss of situational awareness, increasing chances for life threatening situations such as texting while driving. Surprisingly, alternative modalities for information delivery have seen little exploration. Touch, in particular, is a promising candidate given that it is our largest sensory organ with impressive spatial and temporal acuity. Although some approaches have been proposed for touch-based information delivery, they are not without limitations including high learning curves, limited applicability and/or limited expression. This is largely due to the lack of a versatile, comprehensive design theory--specifically, a theory that addresses the design of touch-based building blocks for expandable, efficient, rich and robust touch languages that are easy to learn and use. Moreover, beyond design, there is a lack of implementation and evaluation theories for such languages. To overcome these limitations, a unified, theoretical framework, inspired by natural, spoken language, is proposed called Somatic ABC's for Articulating (designing), Building (developing) and Confirming (evaluating) touch-based languages. To evaluate the usefulness of Somatic ABC's, its design, implementation and evaluation theories were applied to create communication languages for two very unique application areas: audio described movies and motor learning. These applications were chosen as they presented opportunities for complementing communication by offloading information, typically conveyed visually and/or aurally, to the skin. For both studies, it was found that Somatic ABC's aided the design, development and evaluation of rich somatic languages with distinct and natural communication units.Dissertation/ThesisPh.D. Computer Science 201

Quantity and Quality: Not a Zero-Sum Game

Quantification of existing theories is a great challenge but also a great chance for the study of language in the brain. While quantification is necessary for the development of precise theories, it demands new methods and new perspectives. In light of this, four complementary methods were introduced to provide a quantitative and computational account of the extended Argument Dependency Model from Bornkessel-Schlesewsky and Schlesewsky. First, a computational model of human language comprehension was introduced on the basis of dependency parsing. This model provided an initial comparison of two potential mechanisms for human language processing, the traditional "subject" strategy, based on grammatical relations, and the "actor" strategy based on prominence and adopted from the eADM. Initial results showed an advantage for the traditional subject" model in a restricted context; however, the "actor" model demonstrated behavior in a test run that was more similar to human behavior than that of the "subject" model. Next, a computational-quantitative implementation of the "actor" strategy as weighted feature comparison between memory units was used to compare it to other memory-based models from the literature on the basis of EEG data. The "actor" strategy clearly provided the best model, showing a better global fit as well as better match in all details. Building upon the success modeling EEG data, the feasibility of estimating free parameters from empirical data was demonstrated. Both the procedure for doing so and the necessary software were introduced and applied at the level of individual participants. Using empirically estimated parameters, the models from the previous EEG experiment were calculated again and yielded similar results, thus reinforcing the previous work. In a final experiment, the feasibility of analyzing EEG data from a naturalistic auditory stimulus was demonstrated, which conventional wisdom says is not possible. The analysis suggested a new perspective on the nature of event-related potentials (ERPs), which does not contradict existing theory yet nonetheless goes against previous intuition. Using this new perspective as a basis, a preliminary attempt at a parsimonious neurocomputational theory of cognitive ERP components was developed

Style-driven Shape Analysis and Synthesis

In this dissertation I will investigate algorithms that analyze stylistic properties of 3D shapes and automatically synthesize shapes given style specifications. I will start by introducing a structure-transcending method for style similarity evaluation between 3D shapes. Inspired by observations about style similarity in art history literature, we propose an algorithmically computed style similarity measure which identifies style related elements on the analyzed models and collates element-level geometric similarity measurements into an object-level style measure consistent with human perception. To achieve this consistency we employ crowdsourcing to learn the relative perceptual importance of a range of elementary shape distances and other parameters used in our measurement from participant answers to cross-structure style similarity queries. I will then describe an algorithm that utilizes this learned style similarity measure to synthesize 3D models of man-made shapes. The algorithm combines user-specified style, described via an exemplar shape, and functionality, encoded by a functionally different target shape. We transfer the exemplar style to the target via a sequence of compatible element-level operations where the compatibility is a learned metric that estimates the impact of each operation on the edited shape. We use this metric to cast style transfer as a tabu search, which incrementally updates the target shape using compatible operations, progressively increasing its style similarity to the exemplar while strictly maintaining its functionality at each step. Finally I will propose a method for reconstructing 3D shapes following style aspects of given 2D drawings. Our method takes line drawings as input and converts them into surface depth and normal maps from several output viewpoints via a deep convolutional neural network with multi-view encoder-decoder architecture. The multi-view maps are then consolidated into a dense coherent 3D point cloud by solving an optimization problem that fuses depth and normal information across all output viewpoints. The output point cloud is then converted into a polygon mesh representation, which is further fine-tuned to match the input sketch more precisely

Biodiversity: Its Measurement and Metaphysics

Biodiversity is a concept that plays a key role in both scientific theories such as the species-area law and conservation politics. Currently, however, little agreement exists on how biodiversity should be defined, let alone measured. This has led to suggestions that biodiversity is not a metaphysically robust concept, with major implications for its usefulness in formulating scientific theories and making conservation decisions. A general discussion of biodiversity is presented, highlighting its application both in scientific and conservation contexts, its relationship with environmental ethics, and existing approaches to its measurement. To overcome the limitations of existing biodiversity concepts, a new concept of biocomplexity is proposed. This concept equates the biodiversity of any biological system with its effective complexity. Biocomplexity is shown to be the only feasible measure of biodiversity that captures the essential features desired of a general biodiversity concept. In particular, it is a well-defined, measurable and strongly intrinsic property of any biological system. Finally, the practical application of biocomplexity is discussed

Biodiversity: Its Measurement and Metaphysics

Biodiversity is a concept that plays a key role in both scientific theories such as the species-area law and conservation politics. Currently, however, little agreement exists on how biodiversity should be defined, let alone measured. This has led to suggestions that biodiversity is not a metaphysically robust concept, with major implications for its usefulness in formulating scientific theories and making conservation decisions. A general discussion of biodiversity is presented, highlighting its application both in scientific and conservation contexts, its relationship with environmental ethics, and existing approaches to its measurement. To overcome the limitations of existing biodiversity concepts, a new concept of biocomplexity is proposed. This concept equates the biodiversity of any biological system with its effective complexity. Biocomplexity is shown to be the only feasible measure of biodiversity that captures the essential features desired of a general biodiversity concept. In particular, it is a well-defined, measurable and strongly intrinsic property of any biological system. Finally, the practical application of biocomplexity is discussed