There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Cell phones and other portable devices are equipped with a variety of technologies by which existing functionality can be improved, and new functionality can be provided. Some relate to visual search capabilities, and determining appropriate actions responsive to different image inputs. Others relate to processing of image data. Still others concern metadata generation, processing, and representation. Yet others relate to coping with fixed focus limitations of cell phone cameras, e.g., in reading digital watermark data. Still others concern user interface improvements. A great number of other features and arrangements are also detailed.

Methods and Systems for Content Processing

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research.

/pdf/the-propositional-nature-of-human-associative-learning-1l9gvlzemy.pdf

The propositional nature of human associative learning.

Skeleton and pattern based parallel programming promise significant benefits but remain absent from mainstream practice. We consider why this situation has arisen and propose a number of design principles which may help to redress it. We sketch the eSkel library, which represents a concrete attempt to apply these principles, eSkel is based on C and MPI, thereby embedding its skeletons in a conceptually familiar framework. We present an application of eSkel and analyse it as a response to our manifesto.

/pdf/bringing-skeletons-out-of-the-closet-a-pragmatic-manifesto-2o363rcxyu.pdf

Bringing skeletons out of the closet: a pragmatic manifesto for skeletal parallel programming

The quarterly journal of experimental psychology

A high-speed analog VLSI image acquisition and pre-processing system has been designed and fabricated in a 0.35 ?m standard CMOS process. The chip features a massively parallel architecture enabling the computation of programmable low-level image processing in each pixel. Extraction of spatial gradients and convolutions such as Sobel or Laplacian filters are implemented on the circuit. For this purpose, each 35 ?m × 35 ?m pixel includes a photodiode, an amplifier, two storage capacitors, and an analog arithmetic unit based on a four-quadrant multiplier architecture. The retina provides address-event coded output on three asynchronous buses: one output dedicated to the gradient and the other two to the pixel values. A 64 × 64 pixel proof-of-concept chip was fabricated. A dedicated embedded platform including FPGA and ADCs has also been designed to evaluate the vision chip. Measured results show that the proposed sensor successfully captures raw images up to 10 000 frames per second and runs low-level image processing at a frame rate of 2000 to 5000 frames per second.

/pdf/a-10-000-fps-cmos-sensor-with-massively-parallel-image-1r2x5o7gk1.pdf

A 10 000 fps CMOS Sensor With Massively Parallel Image Processing

In many applications such as video surveillance or defect detection, the perception of information related to a scene is limited in areas with strong contrasts. The high dynamic range (HDR) capture technique can deal with these limitations. The proposed method has the advantage of automatically selecting multiple exposure times to make outputs more visible than fixed exposure ones. A real-time hardware implementation of the HDR technique that shows more details both in dark and bright areas of a scene is an important line of research. For this purpose, we built a dedicated smart camera that performs both capturing and HDR video processing from three exposures. What is new in our work is shown through the following points: HDR video capture through multiple exposure control, HDR memory management, HDR frame generation, and rep- resentation under a hardware context. Our camera achieves a real-time HDR video output at 60 fps at 1.3 mega- pixels and demonstrates the efficiency of our technique through an experimental result. Applications of this HDR smart camera include the movie industry, the mass-consumer market, military, automotive industry, and sur- veillance

https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01067716/document

Hardware-based smart camera for recovering high dynamic range video from multiple exposures

This paper is a general overview of the SKIPPER project, run at Blaise Pascal University between 1996 and 2002. The main goal of the SKIPPER project was to demonstrate the applicability of skeleton-based parallel programming techniques to the fast prototyping of reactive vision applications. This proiect has produced several versions of a full-fledged integrated parallel programming environment (PPE). These PPEs have been used to implement realistic vision applications, such as road following or vehicle tracking for assisted driving, on embedded parallel platforms embarked on semi-autonomous vehicles. All versions of SKIPPER share a common front-end and repertoire of skeletons--presented in previous papers--but differ in the techniques used for implementing skeletons. This paper focuses on these implementation issues, by making a comparative survey, according to a set of four criteria (efficiency, expressivity, portability, predictability), of these implementation techniques. It also gives an account of the lessons we have learned, both when dealing with these implementation issues and when using the resulting tools for prototyping vision applications.

/pdf/skeletons-for-parallel-image-processing-an-overview-of-the-1gdtcuv4po.pdf

Skeletons for parallel image processing: an overview of the SKIPPER project

This paper describes a complete FPGA-based smart camera architecture named HDR-ARtiSt (High Dynamic Range Adaptive Real-time Smart camera) which produces a real-time high dynamic range (HDR) live video stream from multiple captures. A specific memory management unit has been defined to adjust the number of acquisitions to improve HDR quality. This smart camera is built around a standard B&W CMOS image sensor and a Xilinx FPGA. It embeds multiple captures, HDR processing, data display and transfer, which is an original contribution compared to the state-of-the-art. The proposed architecture enables a real-time HDR video flow for a full sensor resolution (1.3 Mega pixels) at 60 frames per second.

/pdf/hdr-artist-an-adaptive-real-time-smart-camera-for-high-3jy6z1wk5m.pdf

HDR-ARtiSt: an adaptive real-time smart camera for high dynamic range imaging

One crucial parameter to evaluate the state of the heart after myocardial infarction (MI) is the viability of the myocardial segment, i.e., if the segment recovers its functionality upon revascularization. MRI performed several minutes after the injection of a contrast agent (delayed enhancement-MRI or DE-MRI) is a method of choice to evaluate the extent of MI, and by extension, to assess viable tissues after an injury. The Emidec dataset is composed of a series of exams with DE-MR images in short axis orientation covering the left ventricle from normal cases or patients with myocardial infarction, with the contouring of the myocardium and diseased areas (if present) from experts in the domains. Moreover, classical available clinical parameters when the patient is managed by an emergency department are provided for each case. To the best of our knowledge, the Emidec dataset is the first one where annotated DE-MRI are combined with clinical characteristics of the patient, allowing the development of methodologies for exam classification as for exam quantification.

https://www.mdpi.com/2306-5729/5/4/89/pdf

Dominique Ginhac

Papers

A 10 000 fps CMOS Sensor With Massively Parallel Image Processing

Hardware-based smart camera for recovering high dynamic range video from multiple exposures

Skeletons for parallel image processing: an overview of the SKIPPER project

HDR-ARtiSt: an adaptive real-time smart camera for high dynamic range imaging

Emidec: A Database Usable for the Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI