Object (computer science)

About: Object (computer science) is a research topic. Over the lifetime, 106024 publications have been published within this topic receiving 1360115 citations. The topic is also known as: obj & Rq.

Audio-visual three-dimensional input/output

Abstract: Methods and apparatus for determining an object's three-dimensional location (i.e. real world coordinates) using the audio-video infrastructure of a 3G cellular phone or a 3C (Computer, Communications, Consumer) electronic device. A first detection device (e.g. a camera) is used to capture images of the objects. The captured image data is used to compute location data of the object in a first two-dimensional plane. A second detection device (e.g. microphone or infrared detector) may be used to collect additional location data in a second plane, which when combined with image data from the captured images allows the determination of the real world coordinates (x, y, z) of the object. The real-world coordinate data may be used in various applications. If the size of an object of interest is known or can be calculated, and the size of the projected image does not vary due to rotation of the object, a single camera (e.g. the camera in a 3G or 3C mobile device) may be used to obtain three-dimensional coordinate data for the applications.

Selective storage and maintenance of an object's features in visual working memory.

Abstract: It has been shown that we have a highly capacity-limited representational space with which to store objects in visual working memory. However, most objects are composed of multiple feature attributes, and it is unknown whether observers can voluntarily store a single attribute of an object without necessarily storing all of its remaining features. In this study, we used a masking paradigm to measure the efficiency of encoding, and neurophysiological recordings to directly measure visual working memory maintenance while subjects viewed multifeature objects and were required to remember only a single feature or all of the features of the objects. We found that measures of both encoding and maintenance varied systematically as a function of which object features were task relevant. These experiments show that individuals can control which features of an object are selectively stored in working memory.

Recent Advances in Deep Learning for Object Detection

Abstract: Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades. Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc. Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning. Keywords: Object Detection, Deep Learning, Deep Convolutional Neural Networks

Semantic-Based Surveillance Video Retrieval

Abstract: Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene

Multispectral Image Analysis Using the Object-Oriented Paradigm

Abstract: Introduction Background Objects and Human Interpretation Process Object-Oriented Paradigm Organization of the Book Multispectral Remote Sensing Spatial Resolution Spectral Resolution Radiometric Resolution Temporal Resolution Multispectral Image Analysis Why an Object-Oriented Approach? Object Properties Advantages of Object-Oriented Approach Creating Objects Image Segmentation Techniques Creating and Classifying Objects at Multiple Scales Object Classification Creating Multiple Levels Creating Class Hierarchy and Classifying Objects Final Classification Using Object Relationships between Levels Object-Based Image Analysis Image Analysis Techniques Supervised Classification Using Multispectral Information Exploring the Spatial Dimension Using Contextual Information Taking Advantage of Morphology Parameters Taking Advantage of Texture Adding Temporal Dimension Advanced Object Image Analysis Techniques to Control Image Segmentation within eCognition Techniques to Control Image Segmentation within eCognition Multi-Scale Approach for Image Analysis Objects vs. Spatial Resolution Exploring the Parent-Child Object Relationships Using Semantic Relationships Taking Advantage of Ancillary Data Accuracy Assessment Sample Selection Sampling Techniques Ground Truth Collection Accuracy Assessment Measures References Index