An optimized stereo vision implementation for embedded systems: application to RGB and Infra-Red images

The aim of this paper is to demonstrate the applicability and the effectiveness of a computationally demanding stereo matching algorithm in different lowcost and low-complexity embedded devices, by focusing on the analysis of timing and image quality performances. Various optimizations have been implemented to allow its deployment on specific hardware architectures while decreasing memory and processing time requirements: (1) reduction of color channel information and resolution for input images, (2) low-level software optimizations such as parallel computation, replacement of function calls or loop unrolling, (3) reduction of redundant data structures and internal data representation. The feasibility of a stereovision system on a low cost platform is evaluated by using standard datasets and images taken from Infra-Red (IR) cameras. Analysis of the resulting disparity map accuracy with respect to a full-size dataset is performed as well as the testing of suboptimal solutions

Reduced vision in older adults with age related macular degeneration interferes with ability to care for self and impairs role as carer

Aim: To study the relation between visual impairment and ability to care for oneself or a dependant in older people with age related macular degeneration (AMD). Method: Cross sectional study of older people with visual impairment due to AMD in a specialised retinal service clinic. 199 subjects who underwent visual function assessment (fully corrected distance and near acuity and contrast sensitivity in both eyes), followed by completion of a package of questionnaires dealing with general health status (SF36), visual functioning (Daily Living Tasks Dependent on Vision, DLTV) and ability to care for self or provide care to others. The outcome measure was self reported ability to care for self and others. Three levels of self reported ability to care were identified—inability to care for self (level 1), ability to care for self but not others (level 2), and ability to care for self and others (level 3). Results: People who reported good general health status and visual functioning (that is, had high scores on SF36 and DLTV) were more likely to state that they were able to care for self and others. Similarly people with good vision in the better seeing eye were more likely to report ability to care for self and others. People with a distance visual acuity (DVA) worse than 0.4 logMAR (Snellen 6/15) had less than 50% probability of assigning themselves to care level 3 and those with DVA worse than 1.0 logMAR (Snellen 6/60) had a probability of greater than 50% or for assigning themselves to care level 1. Regression analyses with level of care as the dependent variable and demographic factors, DLTV subscales, and SF36 dimensions as the explanatory variables confirmed that the DLTV subscale 1 was the most important variable in the transition from care level 3 to care level 2. The regression analyses also confirmed that the DLTV subscale 2 was the most important in the transition from care level 3 to care level 1. Conclusions: Ability to care for self and dependants has a strong relation with self reported visual functioning and quality of life and is adversely influenced by visual impairment. The acuity at which the balance of probability shifts in the direction of diminished ability to care for self or others is lower than the level set by social care agencies for provision of support. These findings have implications for those involved with visual rehabilitation and for studies of the cost effectiveness of interventions in AMD.

A machine vision approach to the grading of crushed aggregate

The grading of crushed aggregate is carried out usually by sieving. We describe a new image-based approach to the automatic grading of such materials. The operational problem addressed is where the camera is located directly over a conveyor belt. Our approach characterizes the information content of each image, taking into account relative variation in the pixel data, and resolution scale. In feature space, we find very good class separation using a multidimensional linear classifier. The innovation in this work includes (i) introducing an effective image-based approach into this application area, and (ii) our supervised classification using wavelet entropy-based features.

An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN)

The histological grading of cervical intraepithelial neoplasia (CIN) remains subjective, resulting in inter- and intra-observer variation and poor reproducibility in the grading of cervical lesions. This study has attempted to develop an objective grading system using automated machine vision. The architectural features of cervical squamous epithelium are quantitatively analysed using a combination of computerized digital image processing and Delaunay triangulation analysis; 230 images digitally captured from cases previously classified by a gynaecological pathologist included normal cervical squamous epithelium (n = 30), koilocytosis (n = 46), CIN 1 (n = 52), CIN 2 (n = 56), and CIN 3 (n=46). Intra- and inter-observer variation had kappa values of 0.502 and 0.415, respectively. A machine vision system was developed in KS400 macro programming language to segment and mark the centres of all nuclei within the epithelium. By object-oriented analysis of image components, the positional information of nuclei was used to construct a Delaunay triangulation mesh. Each mesh was analysed to compute triangle dimensions including the mean triangle area, the mean triangle edge length, and the number of triangles per unit area, giving an individual quantitative profile of measurements for each case. Discriminant analysis of the geometric data revealed the significant discriminatory variables from which a classification score was derived. The scoring system distinguished between normal and CIN 3 in 98.7% of cases and between koilocytosis and CIN 1 in 76.5% of cases, but only 62.3% of the CIN cases were classified into the correct group, with the CIN 2 group showing the highest rate of misclassification. Graphical plots of triangulation data demonstrated the continuum of morphological change from normal squamous epithelium to the highest grade of CIN, with overlapping of the groups originally defined by the pathologists. This study shows that automated location of nuclei in cervical biopsies using computerized image analysis is possible. Analysis of positional information enables quantitative evaluation of architectural features in CIN using Delaunay triangulation meshes, which is effective in the objective classification of CIN. This demonstrates the future potential of automated machine vision systems in diagnostic histopathology. Copyright (C) 2000 John Wiley and Sons, Ltd.