Natural Vision Evolved Beta [UPD] Download
Marylee Newness
Most natural images, although apparently diverse, have surprisingly regular 1/ f β Fourier amplitude spectra (average β: 1.05-1.20). If the visual system evolved and develops in this spectral environment, is there a sense in which human vision displays commensurate fractal properties? Correlation analysis was applied to human spatial frequency contrast sensitivity. Adults - but not infants - show power law correlational structure consistent with a β of 1.09-1.20 (closely matched to that of natural images). This result provides insights into multi-scale spatial frequency processing in human vision and provides a basis for understanding perceptual phenomena like contrast control and motion deblurring.
To illustrate this here is a nice video =-jHDyhJdDb8 that reflects the importance of technology touching the human emotions. This YouTube video posted was a popular brand that offers a glass for the color-blind people which can correct the colorblind vision issue in the real time for the end user. Interestingly this aspect is about "Accessibility" that is one of the aspects that is typically covered during a Beta Testing. Just by looking at this aspect "Accessibility", in context to the video, naturally, the question comes "What can we do for this father and the son, as a tester or a developer or a designer?". And when we look at the stats, we find the number of people the accessibility impacts are huge-- Every one in five-person is challenged by some kind of disability. But unfortunately in some reports indicate that at more than 90% of the websites in 2011, were not conformant to W3C's accessibility guidelines.
The International Standards Organization (ISO) has been getting the standards around Quality vs. Usability evolved over time. During the 1998 ISO identified the efficiency, effectiveness, and satisfaction as major attributes of usability. Just a year after that, in 1999, the quality model proposed involved an approach to measure quality in the light of internal software quality and external factors. In 2001 the ISO/IEC 9126-4 standard suggested that the difference between usability and the quality in use is a matter of context of use. The ISO/IEC 9126-4 (2001) standard also distinguished external versus internal quality and defined related metrics. Metrics for external quality can be obtained only by executing the software product in the system environment for which the product is intended. This shows that without Usability / HCI in the right context, the Quality process is incomplete. One interesting point to notice here that that "context" referred here is actually that is fundamental to a beta testing i.e. "real users in a real environment", thereby making the case of Beta Testing stronger.
Mostly this vision of the ideal beta testing solution touches upon all the aspect we just discussed. It also touches upon all the interaction points of the different persona e.g. customer, end-user, developer, tester, product owner, project manager, support engineer etc. across the whole Product Life Cycle and utilizes automation along with the Machine Learning components such as Computer Vision (CV) and Natural Language Processing (NLP) to gather information that has to be processed by the cognitive aspect to generate the desired insights about the product and recommendations. During this process, the system will involve data from standards and specs along with the design benchmark generated from the inputs at the design phase of the SDLC, so that meaningful insights can be generated.
Typically, a single natural image is not sufficient to train a program to extract edge features in edge detection when only training images and their ground truth are provided. However, a single training image might be considered as proper training data when domain knowledge, such as used in Gaussian-based edge detection, is provided. In this paper, we employ Genetic Programming (GP) to automatically evolve Gaussian-based edge detectors to extract edge features based on training data consisting of a single image only. The results show that a single image with a high proportion of true edge points can be used to train edge detectors which are not significantly different from rotation invariant surround suppression. When the programs separately evolved from eight single images are considered as weak classifiers, the combinations of these programs perform better than rotation invariant surround suppression.
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