Computer Vision

Micro aerial vehicles, particularly quadrotors, have been used in a wide range of applications. However, the literature on aerial manipulation and grasping is limited and the work is based on quasi-static models. In this paper, we draw inspiration from agile, fast-moving birds such as raptors, that are able to capture moving prey on the ground or in water, and develop similar capabilities for quadrotors. We address dynamic grasping, an approach to prehensile grasping in which the dynamics of the robot and its gripper are significant and must be explicitly modeled and controlled for successful execution. Dynamic grasping is relevant for fast pick-and-place operations, transportation and delivery of objects, and placing or retrieving sensors. We show how this capability can be realized (a) using a motion capture system and (b) without external sensors relying only on onboard sensors. In both cases we describe the dynamic model, and trajectory planning and control algorithms. In partic...

Today, the use of artificial intelligence methods to diagnose and predict infectious and non-infectious diseases has attracted so much attention. Currently, COVID-19 is considered a new virus which has caused so many deaths worldwide. Due to the pandemic nature of COVID-19, the automated tools for the clinical diagnostic of this disease are highly desired. Convolutional Neural Networks (CNNs) have shown outstanding classification performance on image datasets. Up to our knowledge, COVID computer aided diagnosis systems based on CNNs and clinical information have been never analyzed or explored to date. Moreover, Most of existing literature on COVID-19 focuses on distinguishing infected individuals from non-infected ones. In this paper, we propose a novel method named CNN-AE to predict survival chance of COVID-19 patients using a CNN trained on clinical information. To further increase the prediction accuracy, we use the CNN in combination with an autoencoder. Our method is one of th...

The paper presents a new image forgery detection technique. The proposed technique uses digital signatures; it generates a digital signature for each column and embeds the signature in the least significant bits of each corresponding column’s selected pixels. The message digest algorithm 5 (MD5) is used for digital signature generation, and the fourleast-significant-bit substitution mechanism is used to embed the signature in the designated pixels. The embedding of the digital signature in the selected pixel remains completely innocent and undetectable for the human visual system. The proposed forgery detection technique has demonstrated significant results against different types of forgeries introduced to digital images and successfully detected and pointed out the forged columns.

The characterizations of the nanoporous membrane require appropriate segmentation of the pores. The segmentation technique used for one category or class of image may not be suitably applied to the other class of images. Selecting the ubiquitous segmentation technique that can be applied to all image types is a challenging issue. In spite of several decades of research, there is no ubiquitously accepted method for image segmentation and therefore it remains a challenge in digital image processing. The objective of the present study is to automate the system with five different segmentation techniques; global thresholding, active contour, K-means, region growing and watershed to extort the pore from the experimental Al2O3 FESEM images, synthesized using different anodizing parameters. The geometrical features; nanopore wall thickness, pore size and porosity are computed for all the experimental images with the proposed segmentation techniques and are compared with the manual results....

En el trabajo se presenta un regulador Neuro–Petri, para el control de las composiciones del destilado y del residuo en un proceso multivariable con fuerte interaccion: una torre de destilacion de mezcla binaria. Se compara el regulador Neuro–Petri y un regulador convencional PI multilazos, con y sin desacopladores, cuyos parametros son ajustados por el metodo Biggest Log Modulus Tuning (BLT), usando para el analisis los siguientes indices de comportamiento: ITAE: integral del tiempo por el error absoluto, el tiempo de establecimiento y la desviacion maxima absoluta. A partir del analisis de los resultados obtenidos en los indices de comportamiento antes mencionados, pudo apreciarse una mejor ejecucion del regulador Neuro–Petri, respecto a los reguladores convencionales, para el control del proceso estudiado. Palabras clave: torre de destilacion; regulador Neuro–Petri; regulador PI multilazos. In the work is presented, a Neuro – Petri regulator is presented for the control of the co...

There are many task in surveillance monitoring such as object detection, person identification, activity and action recognition etc. Integrating variety of surveillance task through a multimodal interactive system will benefit real-life deployment, and will also support human operators. We first introduce a dataset which is first of its kind and named as Surveillance Video Question Answering (SVideoQA) dataset. The multi-camera surveillance monitoring aspect is considered through the multimodal context of Video Question Answering (VideoQA) in the SVideoQA dataset. This paper proposes a deep learning model where VideoQA task on the SVideoQA dataset is attempted to solved in a manner where memorydriven relationship among appearance and motion aspect of the video features are captured. At each level of the relational reasoning respective attentive parts of the context of the motion and appearance features are identified forwarded through frame level and clip level relational reasoning ...

Fair division is a subfield of multiagent systems that is concerned with object distribution. When objects are indivisible, the Maximin Share Guarantee (MMS) is a desirable fairness notion; however, it is not guaranteed to exist. While MMS allocations may not always exist, a relaxation of MMS is guaranteed to exist. We show that there exists a family of instances for which this relaxation fails to guarantee the MMS value for all but a small constant number of agents.

NON-VON,is a ,massively ,parallel ,machine constructed using custom VLSI chips, each containing a number,of simple ,processing ,elements ,A preliminary prototype,is now ,operational ,at Columbia ,University The,machine ,is intended ,to provide ,highly ,efficient support,for ,a wide ,range ,of artificial ,intelligence ,and other,symbolic ,applications ,This ,paper ,briefly describes,the ,current ,version ,of the ,NON-VON machine,and ,presents ,evidence ,for ,its applicability ,to the execution of OPS5 production systems, a number oflow- and intermediate-level computer vision tasks, and,certain ,"difficult" relational ,algebraic ,operations relevant,to knowledge ,base ,management ,Analytic ,and simulation,results ,are ,presented ,for ,a number ,of algorithms,The data ,suggest ,that NON-VON could provide,a performance,improvement,of as much,as two tothree,orders ,of magnitude ,over ,a conventional sequential,machine,for a wide,range,of AI tasks

Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. The learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. The cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. Therefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). The learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. The output operation of e-learning with teaching equipment...

Ellipsometry techniques allow to measure polarization information of materials, requiring precise rotations of optical components with different configurations of lights and sensors. This results in cumbersome capture devices, carefully calibrated in lab conditions, and in very long acquisition times, usually in the order of a few days per object. Recent techniques allow to capture polarimetric spatially-varying reflectance information, but limited to a single view, or to cover all view directions, but limited to spherical objects made of a single homogeneous material. We present sparse ellipsometry , a portable polarimetric acquisition method that captures both polarimetric SVBRDF and 3D shape simultaneously. Our handheld device consists of off-the-shelf, fixed optical components. Instead of days, the total acquisition time varies between twenty and thirty minutes per object. We develop a complete polarimetric SVBRDF model that includes diffuse and specular components, as well as s...

This work is focused on assigning a depth label to each pixel in the image. We consider off-the-shelf algorithms that provide depth information from multiple views or depth information directly obtained from RGB-d sensors. Both of them are common scenarios of a well studied problem where many times we get incomplete depth information. Then, user interaction becomes necessary to finish, improve or correct the solution for certain applications where accurate and dense depth information for all pixels in the image is needed. This work presents our approach to improve the depth assigned to each pixel in an automated manner. Our proposed pipeline combines state-of-the art methods for image superpixel segmentation and energy minimization. Superpixel segmentation reduces complexity and provides more robustness to the labeling decisions. We study how to propagate the depth information to incomplete or inconsistent regions of the image using a Markov Random Field (MRF) energy minimization fr...

ABSTRACTIn order to grasp an object successfully, we must select appropriate contact regions for our hands on the surface of the object. However, identifying such regions is challenging. Here, we describe a workflow to estimate contact regions from marker-based tracking data. Participants grasp real objects, while we track the 3D position of both the objects and the hand including the fingers’ joints. We first determine joint Euler angles from a selection of tracked markers positioned on the back of the hand. Then, we use state-of-the-art hand mesh reconstruction algorithms to generate a mesh model of the participant’s hand in the current pose and 3D position. Using objects that were either 3D printed, or 3D scanned—and are thus available as both real objects and mesh data—allows us to co-register the hand and object meshes. In turn, this allows us to estimate approximate contact regions by calculating intersections between the hand mesh and the co-registered 3D object mesh. The met...

The efficient coding hypothesis posits that sensory systems are tuned to the regularities of their natural input. The statistics of natural image databases have been the topic of many studies, which have revealed biases in the distribution of orientations that are related to neural representations as well as behavior in psychophysical tasks. However, commonly used natural image databases contain images taken with a camera with a planar image sensor and limited field of view. Thus, these images do not incorporate the physical properties of the visual system and its active use reflecting body and eye movements. Here, we investigate quantitatively, whether the active use of the visual system influences image statistics across the visual field by simulating visual behaviors in an avatar in a naturalistic virtual environment. Images with a field of view of 120° were generated during exploration of a virtual forest environment both for a human and cat avatar. The physical properties of th...