A bio-inspired image coder with temporal scalability

We present a novel bio-inspired and dynamic coding scheme for static images. Our coder aims at reproducing the main steps of the visual stimulus processing in the mammalian retina taking into account its time behavior. The main novelty of this work is to show how to exploit the time behavior of the retina cells to ensure, in a simple way, scalability and bit allocation. To do so, our main source of inspiration will be the biologically plausible retina model called Virtual Retina. Following a similar structure, our model has two stages. The first stage is an image transform which is performed by the outer layers in the retina. Here it is modelled by filtering the image with a bank of difference of Gaussians with time-delays. The second stage is a time-dependent analog-to-digital conversion which is performed by the inner layers in the retina. Thanks to its conception, our coder enables scalability and bit allocation across time. Also, our decoded images do not show annoying artefacts such as ringing and block effects. As a whole, this article shows how to capture the main properties of a biological system, here the retina, in order to design a new efficient coder.Comment: 12 pages; Advanced Concepts for Intelligent Vision Systems (ACIVS 2011

Information recovery from rank-order encoded images

The time to detection of a visual stimulus by the primate eye is recorded at 100 – 150ms. This near instantaneous recognition is in spite of the considerable processing required by the several stages of the visual pathway to recognise and react to a visual scene. How this is achieved is still a matter of speculation. Rank-order codes have been proposed as a means of encoding by the primate eye in the rapid transmission of the initial burst of information from the sensory neurons to the brain. We study the efficiency of rank-order codes in encoding perceptually-important information in an image. VanRullen and Thorpe built a model of the ganglion cell layers of the retina to simulate and study the viability of rank-order as a means of encoding by retinal neurons. We validate their model and quantify the information retrieved from rank-order encoded images in terms of the visually-important information recovered. Towards this goal, we apply the ‘perceptual information preservation algorithm’, proposed by Petrovic and Xydeas after slight modification. We observe a low information recovery due to losses suffered during the rank-order encoding and decoding processes. We propose to minimise these losses to recover maximum information in minimum time from rank-order encoded images. We first maximise information recovery by using the pseudo-inverse of the filter-bank matrix to minimise losses during rankorder decoding. We then apply the biological principle of lateral inhibition to minimise losses during rank-order encoding. In doing so, we propose the Filteroverlap Correction algorithm. To test the perfomance of rank-order codes in a biologically realistic model, we design and simulate a model of the foveal-pit ganglion cells of the retina keeping close to biological parameters. We use this as a rank-order encoder and analyse its performance relative to VanRullen and Thorpe’s retinal model

A design ideation method for novice designers

Design ideation is a core stage in the design process that begins with a design brief and results in a range of design concepts from which solutions can be selected. The success of design ideation relies upon designers’ creativity and ingenuity. In current practice, design ideation tends to be an ad hoc process which combines the designer’s experience with techniques such as sketching, brainstorming, and mock-up to develop creative solutions in response to the brief. There are notable differences in ideation performance between novice and expert designers in that experts tend to follow a more systematic process, and have more experience and knowledge of previous designs to draw on. Design ideation is more challenging for novice designers who have limited experience on which to draw and no systematic process to follow. This thesis provides a method that enhances the design ideation performance of novice designers by providing a systematic design ideation process for them to follow, and a database and associated visualisation method that gives them access to previous designs. The method was assessed through empirical evaluation experiments conducted with 101 students in the UK and South Korea. This confirmed that the method improves novice designers’ generation of creative solution concepts in response to a design brief. The research makes four contributions. The method, Knowledge-Enabled Design Ideation Method (KEDIM), provides a systematic design ideation process that includes three steps. The first step draws on a Database of Design Cases (DOS) that is supported by a database schema. DOS is a part of the research contribution that provides a structure to capture case data. DOS was validated through population with 540 design cases, and through use in the second stage of KEDIM, Perceptual Mapping Generation Software (PMGS). The core contribution of PMGS is its visualisation method that brings together selected design cases from the database and presents them in a way that enhances novice designers’ abilities to draw analogies. The final contribution is Systematic Brainstorming (SBI), where these analogies are developed through a set of specific ideation themes alongside solution concepts. KEDIM, through these three tools, improves the effectiveness of novice designers ideation by increasing the number of solution concepts generated when compared with students not using KEDIM responding to the same brief

Physical-based optimization for non-physical image dehazing methods

Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast. To improve their visibility, a process called image dehazing can be applied. Some of the most successful image dehazing algorithms are based on image processing methods but do not follow any physical image formation model, which limits their performance. In this paper, we propose a post-processing technique to alleviate this handicap by enforcing the original method to be consistent with a popular physical model for image formation under haze. Our results improve upon those of the original methods qualitatively and according to several metrics, and they have also been validated via psychophysical experiments. These results are particularly striking in terms of avoiding over-saturation and reducing color artifacts, which are the most common shortcomings faced by image dehazing methods