(1998). Human cognitive abilities: A survey of factor analytic studies. Gifted and Talented International: Vol. 13, No. 2, pp. 97-98.

Human cognitive abilities: A survey of factor analytic studies

Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks. This review highlights what, in the context of CNNs, it means to be a good model in computational neuroscience and the various ways models can provide insight. Specifically, it covers the origins of CNNs and the methods by which we validate them as models of biological vision. It then goes on to elaborate on what we can learn about biological vision by understanding and experimenting on CNNs and discusses emerging opportunities for the use of CNNs in vision research beyond basic object recognition.

Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future.

Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision-giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.

Deep Learning: The Good, the Bad, and the Ugly.

Human activity recognition (HAR) is one of the most important and challenging problems in the computer vision. It has critical application in wide variety of tasks including gaming, human–robot interaction, rehabilitation, sports, health monitoring, video surveillance, and robotics. HAR is challenging due to the complex posture made by the human and multiple people interaction. Various artifacts that commonly appears in the scene such as illuminations variations, clutter, occlusions, background diversity further adds the complexity to HAR. Sensors for multiple modalities could be used to overcome some of these inherent challenges. Such sensors could include an RGB-D camera, infrared sensors, thermal cameras, inertial sensors, etc. This article introduces a comprehensive review of different multimodal human activity recognition methods where different types of sensors being used along with their analytical approaches and fusion methods. Further, this article presents classification and discussion of existing work within seven rational aspects: (a) what are the applications of HAR; (b) what are the single and multi-modality sensing for HAR; (c) what are different vision based approaches for HAR; (d) what and how wearable sensors based system contributes to the HAR; (e) what are different multimodal HAR methods; (f) how a combination of vision and wearable inertial sensors based system contributes to the HAR; and (g) challenges and future directions in HAR. With a more and comprehensive understanding of multimodal human activity recognition, more research in this direction can be motivated and refined.

A Review of Multimodal Human Activity Recognition with Special Emphasis on Classification, Applications, Challenges and Future Directions

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.

pdf/survey-of-computer-vision-algorithms-and-applications-for-1tfxhw683x.pdf

Survey of computer vision algorithms and applications for unmanned aerial vehicles

Falls are serious and costly for elderly people. The Centers for Disease Control and Prevention of the US reports that millions of older people, 65 and older, fall each year at least once. Serious injuries such as; hip fractures, broken bones or head injury, are caused by 20% of the falls. The time it takes to respond and treat a fallen person is crucial. With this paper we present a new, non-invasive system for fallen people detection. Our approach uses only stereo camera data for passively sensing the environment. The key novelty is a human fall detector which uses a CNN based human pose estimator in combination with stereo data to reconstruct the human pose in 3D and estimate the ground plane in 3D. Furthermore, our system consists of a reasoning module which formulates a number of measures to reason whether a person is fallen. We have tested our approach in different scenarios covering most activities elderly people might encounter living at home. Based on our extensive evaluations, our systems shows high accuracy and almost no miss-classification. To reproduce our results, the implementation is publicly available to the scientific community.

/pdf/vision-based-fallen-person-detection-for-the-elderly-2d72912d7l.pdf

Vision-Based Fallen Person Detection for the Elderly

Perceptual judgment of image similarity by humans relies on rich internal representations ranging from low-level features to high-level concepts, scene properties and even cultural associations. However, existing methods and datasets attempting to explain perceived similarity use stimuli which arguably do not cover the full breadth of factors that affect human similarity judgments, even those geared toward this goal. We introduce a new dataset dubbed Totally-Looks-Like (TLL) after a popular entertainment website, which contains images paired by humans as being visually similar. The dataset contains 6016 image-pairs from the wild, shedding light upon a rich and diverse set of criteria employed by human beings. We conduct experiments to try to reproduce the pairings via features extracted from state-of-the-art deep convolutional neural networks, as well as additional human experiments to verify the consistency of the collected data. Though we create conditions to artificially make the matching task increasingly easier, we show that machine-extracted representations perform very poorly in terms of reproducing the matching selected by humans. We discuss and analyze these results, suggesting future directions for improvement of learned image representations.

Totally Looks Like - How Humans Compare, Compared to Machines.

The virtual reconstruction of underwater environments in 3 dimensions can be of great utility for scientific or industrials applications, being the use of Autonomous Underwater Vehicles (AUVs) equipped with cameras an invaluable tool with progressive improvements. However, a highly accurate vehicle localization process is fundamental to place every portion of that 3D model in its real position with respect to the origin of the world system of coordinates, and to join properly the overall reconstructed area, especially in large surveys. Simultaneous Localization And Mapping (SLAM) techniques constitute the most precise localization approach using only data provided by the navigation sensors installed on board the underwater robot. However, the real challenge consists in applying these techniques in underwater environments where the imaging conditions are usually limited or degraded. This paper presents the comparison of two different SLAM approaches based on stereo-vision, a graph-SLAM and an EKF SLAM, applied to localize, in real time, an AUV moving in underwater environments. Both algorithms deal with pure 3D data, (x, y, z) for the vehicle position and a quaternion to represent its orientation. The aim of this work is to assess and compare the performance of both solutions in terms of accuracy in the estimation of the robot pose. First experiments were conducted in controlled aquatic scenarios where it was feasible to build a highly reliable ground truth, showing how the graph-SLAM approach outperforms its EKF counterpart under the same working and environmental conditions. A second set of experiments was conducted in the sea, showing the same tendency in the results. The SLAM pose estimates, given with respect to a global world frame, corresponding to the approach with less accumulated error are used to recreated virtual 3D maps of the environment based on the concatenation of successive stereo visual point clouds placed in the corresponding SLAM locations.

Stereo SLAM for robust dense 3D reconstruction of underwater environments

On the control of attentional processes in vision.

Falls are serious and costly for elderly people. The Centers for Disease Control and Prevention of the US reports that millions of older people, 65 and older, fall each year at least once. Serious injuries such as; hip fractures, broken bones or head injury, are caused by 20% of the falls. The time it takes to respond and treat a fallen person is crucial. With this paper we present a new , non-invasive system for fallen people detection. Our approach uses only stereo camera data for passively sensing the environment. The key novelty is a human fall detector which uses a CNN based human pose estimator in combination with stereo data to reconstruct the human pose in 3D and estimate the ground plane in 3D. Furthermore, our system consists of a reasoning module which formulates a number of measures to reason whether a person is fallen. We have tested our approach in different scenarios covering most activities elderly people might encounter living at home. Based on our extensive evaluations, our systems shows high accuracy and almost no miss-classification. To reproduce our results, the implementation is publicly available to the scientific community.

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Vision-Based Fallen Person Detection for the Elderly

Totally Looks Like - How Humans Compare, Compared to Machines.

Stereo SLAM for robust dense 3D reconstruction of underwater environments

On the control of attentional processes in vision.

Vision-Based Fallen Person Detection for the Elderly