Concerns about neighborhood erosion and conflict in ethnically diverse settings occupy scholars, policy makers, and pundits alike; but the empirical evidence is inconclusive. This article proposes the contested boundaries hypothesis as a refined contextual explanation focused on poorly defined boundaries between ethnic and racial groups. The authors argue that neighborhood conflict is more likely to occur at fuzzy boundaries defined as interstitial or transitional areas sandwiched between two homogeneous communities. Edge detection algorithms from computer vision and image processing allow them to identify these boundaries. Data from 4.7 million time- and geo-coded 311 service requests from New York City support their argument: complaints about neighbors making noise, drinking in public, or blocking the driveway are more frequent at fuzzy boundaries rather than crisp, polarized borders. By focusing on the broader sociospatial structure, the contested boundaries hypothesis overcomes the “aspatial” treatmen...

Contested Boundaries: Explaining Where Ethnoracial Diversity Provokes Neighborhood Conflict.

This work presents a new flexible parameterizable architecture for image and video processing with reduced latency and memory requirements, supporting a variable input resolution. The proposed architecture is optimized for feature detection, more specifically, the Canny edge detector and the Harris corner detector. The architecture contains neighborhood extractors and threshold operators that can be parameterized at runtime. Also, algorithm simplifications are employed to reduce mathematical complexity, memory requirements, and latency without losing reliability. Furthermore, we present the proposed architecture implementation on an FPGA-based platform and its analogous optimized implementation on a GPU-based architecture for comparison. A performance analysis of the FPGA and the GPU implementations, and an extra CPU reference implementation, shows the competitive throughput of the proposed architecture even at a much lower clock frequency than those of the GPU and the CPU. Also, the results show a clear advantage of the proposed architecture in terms of power consumption and maintain a reliable performance with noisy images, low latency and memory requirements.

A Multi-Resolution FPGA-Based Architecture for Real-Time Edge and Corner Detection

A high payload steganographic algorithm based on edge detection

Virtual Reality has the potential to provide descriptive and practical information for medical training and therapy while relieving the patient or the physician. Multimodal interactions between the user and the virtual environment facilitate the generation of high-fidelity sensory impressions, by using not only visual and auditory, but also kinesthetic, tactile, and even olfactory feedback modalities. On the basis of the existing physiological constraints, Virtual Reality in Medicine derives the technical requirements and design principles of multimodal input devices, displays, and rendering techniques. Resulting from a course taught by the authors, Virtual Reality in Medicine presents examples for surgical training, intra-operative augmentation, and rehabilitation that are already in use as well as those currently in development. It is well suited as introductory material for engineering and computer science students, as well as researchers who want to learn more about basic technologies in the area of virtual reality applied to medicine. It also provides a broad overview to non-engineering students as well as clinical users, who desire to learn more about the current state of the art and future applications of this technology.

Virtual Reality in Medicine

Edges of the image play an important role in the field of digital image processing and computer vision. The edges reduce the amount of data, extract useful information from the image and preserve significant structural properties of an input image. Further, these edges can be used for object and facial expression detection. In this paper, we will propose new intuitionistic fuzzy divergence and entropy measures with its proof of validity for intuitionistic fuzzy sets. A new and significant technique has been developed for edge detection. To check the robustness of the proposed method, obtained results are compared with Canny, Sobel and Chaira methods. Finally, mean square error (MSE) and peak signal-to-noise ratio (PSNR) have been calculated and PSNR values of proposed method are always equal or greater than the PSNR values of existing methods. The detected edges of the various sample images are found to be true, smooth and sharpen.

New Divergence and Entropy Measures for Intuitionistic Fuzzy Sets on Edge Detection

Edge detection is one of the most commonly used operations in image analysis, and there are probably more algorithms in the literature for enhancing and detecting edges than any other single subject. The reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detections is an essential tool. In this paper, we have compared several techniques for edge detection in image processing. We consider various well-known measuring metrics used in image processing applied to standard images in this comparison.

/pdf/edge-detection-techniques-evaluations-and-comparison-toa4814qqu.pdf

Edge Detection Techniques: Evaluations and Comparison

In this paper, we implemented Wiener filter, anisotropic diffusion filter, k-distribution based adaptive filter and wavelet filter to de-speckle in medical ultrasound images. The Wiener filter can improve the image qualities well and simulated power spectrum of speckle can be applied on many situations. The Anisotropic diffusion filter can also de-speckle well as long as we choose reasonable parameters, and it doesn’t need extra information of noise pattern. The K-distribution based adaptive filter can improve the image quality, the method is easy to implement and the statistics is easy to estimate and characterize. The wavelet filter is not suitable for removing the speckle in ultrasound images. Keywords: image processing, image restoration, wiener filter, image de-noisng I. I NTRODUCTION The medical Ultrasound B-scan (brightness scan) echo imaging is acquired by summation of the echo signals from ultrasound scatterers in the ultrasound beam range. The scatterers are from structures, tissue interfaces and tissue microstructures etc. in the body, these scatterers are locally correlated. And the coherent summation of signals

Despeckle Filtering in Medical Ultrasound Imaging

Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review

This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data acquisition. A novel noise reduction technique is proposed for alleviating the effects of horizontal and vertical periodic noise in the 2D spatiotemporal activity profiles created by vectorizing and concatenating the LRIR frames. Two main analysis strategies are explored for HAR, including (1) manual feature extraction using texture-based and orthogonal-transformation-based techniques, followed by classification using support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and logistic regression (LR), and (2) deep neural network (DNN) strategy based on a convolutional long short-term memory (LSTM). The proposed periodic noise reduction technique showcases an increase of up to 14.15% using different models. In addition, for the first time, the optimum number of sensors, sensor layout, and distance to subjects are studied, indicating the optimum results based on a single side sensor at a close distance. Reasonable accuracies are achieved in the case of sensor displacement and robustness in detection of multiple subjects. Furthermore, the models show suitability for data collected in different environments.

/pdf/human-activity-recognition-for-ai-enabled-healthcare-using-crm73xsb.pdf

Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data

Remote Human Activity Recognition (HAR) in a private residential area has a beneficial influence on the elderly population's life, since this group of people require regular monitoring of health conditions. This paper addresses the problem of continuous detection of daily human activities using mm-wave Doppler radar. Unlike most previous research, this work records the data in terms of continuous series of activities rather than individual activities. These series of activities are similar to real-life activity patterns. The Dynamic Time Warping (DTW) algorithm is used for the detection of human activities in the recorded time series of data and compared to other time-series classification methods. DTW requires less amount of labelled data. The input for DTW was provided using three strategies, and the obtained results were compared against each other. The first approach uses the pixel-level data of frames (named UnSup-PLevel). In the other two strategies, a Convolutional Variational Autoencoder (CVAE) is used to extract Un-Supervised Encoded features (UnSup-EnLevel) and Supervised Encoded features (Sup-EnLevel) from the series of Doppler frames. Results demonstrates the superiority of the Sup-EnLevel features over UnSup-EnLevel and UnSup-PLevel strategies. However, the performance of the UnSup-PLevel strategy worked surprisingly well without using annotations.

Sara Sharifzadeh

Papers

Edge Detection Techniques: Evaluations and Comparison

Despeckle Filtering in Medical Ultrasound Imaging

Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review

Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data

Continuous Human Activity Recognition using Radar Imagery and Dynamic Time Warping