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The pointing task is the process of pointing to an object on a computer monitor using a pointing device, or physically touching an object with the hand or finger. It is an important element for users when manipulating visual computer interfaces such as traditional screens and touch-screens. In this context, Fitts’ Law remains one of the central studies that have mathematically modeled the pointing method, and was found to be a good predictor of the average time needed to perform a pointing task. Yet, in our modern computerized society, accessing visual information becomes a central need for all kinds of users, namely users who are blind or visually impaired. Hence, the goal of our study is to evaluate whether Fitts’ Law can be applied for blind candidates using the vibration modality on a touch-screen. To achieve this, we first review the literature on Fitts’ Law and visual data accessibility solutions for blind users. Then, we introduce an experimental framework titled FittsEVAL studying the ability of blind users to tap specific shapes on a touch-screen, while varying different parameters, namely: target distance, target size, and the angle of attack of the pointing task. Experiments on blindfolded and blind candidates show that Fitts’ Law can be effectively applied for blind users using a vibrating touch-screen under certain parameters (i.e., when varying target distance and size), while it is not verified under others (i.e., when varying the angle of attack). This can be considered as a first step toward a more complete experimental evaluation of vibrating touch-screen accessibility, toward designing more adapted interfaces for the blind. [ABSTRACT FROM AUTHOR]

Accessing visual information becomes a central need for all kinds of tasks and users (from accessing graphics and charts in news articles, to viewing images of items on sale on e-commerce sites), especially for blind users. In this context, digital tools of assistance, using adapted software (screen readers, talking browsers, etc.), hardware (force feedback mouse, piezo-electric pins, etc.), and more recently touch-screen technology (using smart phones or smart tablets) have been increasingly helping blind persons access and manipulate information. While effective with textual information, yet existing solutions remain limited when handling visual information. In this context, the goal of our study is to shed light on how the vibration modality can be perceived by blind users when accessing simple contour-based images and visual graphics on a touch-screen. In this paper, we target the vibration-only modality, compared with audio-kinesthetic or multimodal vibro-audio solutions. Our main motivation is that the potentials and limitations of touch-screen vibration-only feedback need to be fully studied and understood prior to integrating other modalities (such as sound, human speech, or other forms of haptic feedback). This could prove very useful in a range of applications: allowing blind people to access geographic maps, to navigate autonomously inside and outside buildings, as well as to access graphs and mathematical charts (for blind students). To achieve our goal, we develop a dedicated experimental protocol, titled EVIAC, testing a blind user's capacity in learning, distinguishing, identifying, and recognizing basic shapes and geometric objects presented on a vibrating touch-screen. Extensive tests were conducted on blindfolded and blind candidates, using a battery of evaluation metrics including: i) accuracy of shape recognition, ii) testers’ average response time, iii) number and duration of finger strokes, iv) surface area covered by the testers’ finger path trails, as well as iv) finger path correlation with the surface of the target shape. Results show that blind users are generally capable of accessing simple shapes and graphics presented on a vibrating touch-screen. However, results also underline various issues, ranging over: prolonged response time (e.g., blind users require 1 min and 22 s on average to recognize a basic shape), reduced touch-screen surface coverage, and low correlation between the surface of the target shape and the tester's vibration trails. The latter issues need to be further investigated to produce optimal recipes for using touch-screen technology to support image accessibility for blind users. [ABSTRACT FROM AUTHOR]

Low-light image (LLI) enhancement is an important image processing task that aims at improving the illumination of images taken under low-light conditions. Recently, a remarkable progress has been made in utilizing deep learning (DL) approaches for LLI enhancement. This paper provides a concise and comprehensive review and comparative study of the most recent DL models used for LLI enhancement. To our knowledge, this is the first comparative study dedicated to DL-based models for LLI enhancement. We address LLI enhancement in two ways: (i) standalone, as a separate task, and (ii) end-to-end, as a pre-processing stage embedded within another high-level computer vision task, namely object detection and classification. The paper consists of six logical parts. First, we provide an overview of the background and literature in LLI enhancement. Second, we describe the test data and experimental setup of the study. Third, we present a quantitative and qualitative comparison of the visual and perceptual quality achieved by 10 of the most recent DL-based LLI enhancement models. Fourth, we present a comparative analysis for object detection and classification performance achieved by 4 different object detection models applied on LLIs and their enhanced counterparts. Fifth, we perform a feature analysis of DL feature maps extracted from normal, low-light, and enhanced images, and perform the occlusion experiment to better understand the effect of LLI enhancement on the object detection and classification task. Finally, we provide our conclusions and highlight future steps and potential directions. • Evaluates Deep Learning (DL) models for Low-light Image (LLI) enhancement. • Compares 10 LLI enhancement models and 4 object detection and classification models. • Provides a quantitative and qualitative comparison of visual and perceptual quality. • Evaluates impact of LLI enhancement on object detecting and classification quality. • Performs occlusion experiment to study LLI enhancement's effect on object detection. [ABSTRACT FROM AUTHOR]