Digital Coding of Waveforms

Human vision system actively seeks interesting regions in images to reduce the search effort in tasks, such as object detection and recognition. Similarly, prominent actions in video sequences are more likely to attract our first sight than their surrounding neighbors. In this paper, we propose a spatiotemporal video attention detection technique for detecting the attended regions that correspond to both interesting objects and actions in video sequences. Both spatial and temporal saliency maps are constructed and further fused in a dynamic fashion to produce the overall spatiotemporal attention model. In the temporal attention model, motion contrast is computed based on the planar motions (homography) between images, which is estimated by applying RANSAC on point correspondences in the scene. To compensate the non-uniformity of spatial distribution of interest-points, spanning areas of motion segments are incorporated in the motion contrast computation. In the spatial attention model, a fast method for computing pixel-level saliency maps has been developed using color histograms of images. A hierarchical spatial attention representation is established to reveal the interesting points in images as well as the interesting regions. Finally, a dynamic fusion technique is applied to combine both the temporal and spatial saliency maps, where temporal attention is dominant over the spatial model when large motion contrast exists, and vice versa. The proposed spatiotemporal attention framework has been applied on over 20 testing video sequences, and attended regions are detected to highlight interesting objects and motions present in the sequences with very high user satisfaction rate.

https://crcv.ucf.edu/papers/spatiotemporal_attention_homo.pdf

Visual attention detection in video sequences using spatiotemporal cues

Identifying moving objects is a critical task for many computer vision applications; it provides a classification of the pixels into either foreground or background. A common approach used to achieve such classification is background removal. Even though there exist numerous of background removal algorithms in the literature, most of them follow a simple flow diagram, passing through four major steps, which are pre-processing, background modelling, foreground de- tection and data validation. In this paper, we survey many existing schemes in the literature of background removal, sur- veying the common pre-processing algorithms used in different situations, presenting different background models, and the most commonly used ways to update such models and how they can be initialized. We also survey how to measure the performance of any moving object detection algorithm, whether the ground truth data is available or not, presenting per- formance metrics commonly used in both cases.

Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art

MPEG-4 is the multimedia standard for combining interactivity, natural and synthetic digital video, audio and computer-graphics Typical applications are: internet, video conferencing, mobile videophones, multimedia cooperative work, teleteaching and games With MPEG-4 the next step from block-based video (ISO/IEC MPEG-1, MPEG-2, CCITT H261, ITU-T H263) to arbitrarily-shaped visual objects is taken This significant step demands a new methodology for system analysis and design to meet the considerably higher flexibility of MPEG-4 Motion estimation is a central part of MPEG-1/2/4 and H261/H263 video compression standards and has attracted much attention in research and industry, for the following reasons: it is computationally the most demanding algorithm of a video encoder (about 60-80% of the total computation time), it has a high impact on the visual quality of a video encoder, and it is not standardized, thus being open to competition Algorithms, Complexity Analysis, and VLSI Architectures for MPEG-4 Motion Estimation covers in detail every single step in the design of a MPEG-1/2/4 or H261/H263 compliant video encoder: Fast motion estimation algorithms Complexity analysis tools Detailed complexity analysis of a software implementation of MPEG-4 video Complexity and visual quality analysis of fast motion estimation algorithms within MPEG-4 Design space on motion estimation VLSI architectures Detailed VLSI design examples of (1) a high throughput and (2) a low-power MPEG-4 motion estimator Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation is an important introduction to numerous algorithmic, architectural and system design aspects of the multimedia standard MPEG-4 As such, all researchers, students and practitioners working in image processing, video coding or system and VLSI design will find this book of interest

Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation

Visual attention is a mechanism in human perception which selects relevant regions from a scene and provides these regions for higher-level processing as object recognition. This enables humans to act effectively in their environment despite the complexity of perceivable sensor data. Computational vision systems face the same problem as humans: there is a large amount of information to be processed and to achieve this efficiently, maybe even in real-time for robotic applications, the order in which a scene is investigated must be determined in an intelligent way. A promising approach is to use computational attention systems that simulate human visual attention. This monograph introduces the biologically motivated computational attention system VOCUS (Visual Object detection with a Computational attention System) that detects regions of interest in images. It operates in two modes, in an exploration mode in which no task is provided, and in a search mode with a specified target. In exploration mode, regions of interest are defined by strong contrasts (e.g., color or intensity contrasts) and by the uniqueness of a feature. For example, a black sheep is salient in a flock of white sheep. In search mode, the system uses previously learned information about a target object to bias the saliency computations with respect to the target. In various experiments, it is shown that the target is on average found with less than three fixations, that usually less than five training images suffice to learn the target information, and that the system is mostly robust with regard to viewpoint changes and illumination variances. Furthermore, we demonstrate how VOCUS profits from additional sensor data: we apply the system to depth and reflectance data from a 3D laser scanner and show the advantages that the laser modes provide. By fusing the data of both modes, we demonstrate how the system is able to consider distinct object properties and how the flexibility of the system increases by considering different data. Finally, the regions of interest provided by VOCUS serve as input to a classifier that recognizes the object in the detected region. We show how and in which cases the classification is sped up and how the detection quality is improved by the attentional front-end. This approach is especially useful if many object classes have to be considered, a frequently occurring situation in robotics. VOCUS provides a powerful approach to improve existing vision systems by concentrating computational resources to regions that are more likely to contain relevant information. The more the complexity and power of vision systems increase in the future, the more they will profit from an attentional front-end like VOCUS.

VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)

This paper addresses the problem of automatic detection and recognition of impulsive sounds, such as glass breaks, human screams, gunshots, explosions or door slams. A complete detection and recognition system is described and evaluated on a sound database containing more than 800 signals distributed among six different classes. Emphasis is set on robust techniques, allowing the use of this system in a noisy environment. The detection algorithm, based on a median filter, features a highly robust performance even under important background noise conditions. In the recognition stage, two statistical classifiers are compared, using Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM), respectively. It can be shown that a rather good recognition rate (98% at 70dB and above 80% for 0dB signal-to-noise ratios) can be reached, even under severe gaussian white noise degradations.

/pdf/automatic-sound-detection-and-recognition-for-noisy-42saj4o6lh.pdf

Automatic sound detection and recognition for noisy environment

This paper investigates the influence of GSM speech coding on text independent speaker recognition performance. The three existing GSM speech coder standards were considered. The whole TIMIT database was passed through these coders, obtaining three transcoded databases. In a first experiment, it was found that the use of GSM coding degrades significantly the identification and verification performance (performance in correspondence with the perceptual speech quality of each coder). In a second experiment, the features for the speaker recognition system were calculated directly from the information available in the encoded bit stream. It was found that a low LPC order in GSM coding is responsible for most performance degradations. By extracting the features directly from the encoded bit-stream, we also managed to obtain a speaker recognition system equivalent in performance to the original one which decodes and reanalyzes speech before performing recognition.

/pdf/gsm-speech-coding-and-speaker-recognition-3rmtlgay9s.pdf

GSM speech coding and speaker recognition

This paper reports an adaptive still color image compression method which produces automatically selected ROI with a higher reconstruction quality with respect to the rest of the input image. The ROI are generated on-the fly with a purely data-driven technique based on visual attention. Inspired from biological vision, the multicue visual attention algorithm detects the most visually salient regions of an image. Thus, when operating in systems with low bit rate constraints, the adaptive coding scheme favors the allocation of a higher number of bits to those image regions that are more conspicuous to the human visual system. The compressed image files produced by this adaptive method are fully compatible with the JPEG standard, which favors their widespread utilization.

/pdf/adaptive-color-image-compression-based-on-visual-attention-2l70rzfaq1.pdf

Adaptive color image compression based on visual attention

this paper describes a phase-locked loop (PLL) designed for clock multiplication in a LVDS transmitter. The PLL consists of a novel low-jitter charge-pump, a fully differential ring-oscillator based VCO, a dynamic-logic PFD, a 2nd order passive loop filter and a digital frequency divider. The PLL exhibits simultaneously low jitter and low power consumption. It has been integrated into a 0.35 ?m CMOS process, occupying 0.09 mm2 of silicon area. For a 350 MHz output frequency, the circuit features a cycle-to-cycle jitter of 7.1 ps rms and 65 ps peak-to-peak. At that frequency, the PLL consumes 12 mW from a supply voltage of 3.3 V.

/pdf/a-low-jitter-and-low-power-cmos-pll-for-clock-multiplication-41uu4o1n2w.pdf

A Low-Jitter and Low-Power CMOS PLL for Clock Multiplication

In this paper we introduce a low complexity and accurate technique for target image search and retrieval. This method, which operates directly in the compressed JPEG domain, addresses two of the CBIR challenges stated by The Benchathlon Network regarding the search of a specific image: finding out if an exact same image exists in a database, and identifying this occurrence even when the database image has been compressed with a different coding bit-rate. The proposed technique can be applied in feature-containing or featureless image collections, and thus it is also suitable to search for image copies that might exist on the Web for law enforcement of copyrighted material. The reported method exploits the fact that the phase of the Discrete Cosine Transform coefficients contains a significant amount of information of a transformed image. By processing only the phase part of these coefficients, a simple, fast, and accurate target image search and retrieval technique is achieved.

/pdf/efficient-compressed-domain-target-image-search-and-th5crvj9y1.pdf

Michael Ansorge

Papers

Automatic sound detection and recognition for noisy environment

GSM speech coding and speaker recognition

Adaptive color image compression based on visual attention

A Low-Jitter and Low-Power CMOS PLL for Clock Multiplication

Efficient compressed domain target image search and retrieval