Cognitive Systems and Signal Processing in Image Processing

2022 ◽  
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Cognitive Systems

Matlab and Parallel Computing

2012 ◽  
Vol 17 (4) ◽  
pp. 207-216 ◽  
Author(s):  
Magdalena Szymczyk ◽  
Piotr Szymczyk
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Control Systems ◽  
High Performance ◽  
Parallel Applications ◽  
Process Simulations ◽  
Key Features ◽  
Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

scholarly journals Image Processing Architectures

2018 ◽  
pp. 68-74
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Image Quality ◽  
Comparative Study ◽  
Focal Plane ◽  
Detailed Review ◽  
Communication Problem ◽  
Fast Processing ◽  
Processing Architectures ◽  
The Comparative Study

One type of signal processing is Image processing in which the input used as an image and the output might also be an image or a set of features that are related to the image. Images are handled as a 2D signal using image processing methods. For the fast processing of images, several architectures are suitable for different responsibilities in the image processing practices are important. Various architectures have been used to resolve the high communication problem in image processing systems. In this paper, we will yield a detailed review about these image processing architectures that are commonly used for the purpose of getting higher image quality. Architectures discussed are FPGA, Focal plane SIMPil, SURE engine. At the end, we will also present the comparative study of MSIMD architecture that will facilitate to understand best one.

scholarly journals Analysis of Potholes on Road Using Image Processing

2021 ◽  
Vol 9 (8) ◽  
pp. 1731-1734
Author(s):  
K. S. Prasath
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Computer Science ◽  
Research Area ◽  
Raspberry Pi ◽  
Image Detection ◽  
The Road ◽  
Text Signals ◽  
Science Disciplines

Abstract: Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Nowadays, image processing is one among rapidly growing technologies. It forms core research area within engineering and computer science disciplines too. Image detection on road is primarily carried out with the help of camera with Raspberry pi 3 model b+ and stimulation software. The device is built in such a way that we can identify any potholes in the respective roads and able to rectify as soon as possible with the help of the device. The data signals shared by the device will be converted to text signals from which we can get it right. These devices are fixed at top of the lamppost which is located at the corners of the road from where the device is monitoring the road at 120 degree for weekly once respectively. Keywords: Image processing, Image detection on road, Raspberry pi 3, 120 degree

Developing a Multimodal Application for a Scientific Experiment on Smartphones

2013 ◽  
pp. 68-80 ◽  
Author(s):  
José Rouillard
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Mobile Applications ◽  
Mobile Application ◽  
Development Process ◽  
Mobile Multimedia ◽  
Tablet Devices ◽  
Scientific Experiment ◽  
Visual Tool

Designing and developing multimodal mobile applications is an important knowledge for researchers and industrial engineers. It is crucial to be able to rapidly develop prototypes for smartphones and tablet devices in order to test and evaluate mobile multimedia solutions, without necessarily being an expert in signal processing (image processing, objects recognition, sensors processing, etc.). This chapter proposes to follow the development process of a scientific experiment, in which a mobile application will be used to determine which modality (touch, voice, QRcode) is preferred for entering expiration dates of alimentary products. For the conception and the generation of the mobile application, the AppInventor framework is used. Benefits and limitations of this visual tool are presented across the “Pervasive Fridge” case study, and the obtained final prototype is discussed.

Artificial Intelligence in Medical Science

2014 ◽  
pp. 11-23 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Decision Making ◽  
Signal Processing ◽  
Expert System ◽  
Knowledge Base ◽  
Medical Diagnosis ◽  
Medical Science ◽  
Inference Engine ◽  
Medical Decision

Applying Artificial Intelligence (AI) for increasing the reliability of medical decision making has been studied for some years, and many researchers have studied in this area. In this chapter, AI is defined and the reason of its importance in medical diagnosis is explained. Various applications of AI in medical diagnosis such as signal processing and image processing are provided. Expert system is defined and it is mentioned that the basic components of an expert system are a “knowledge base” or KB and an “inference engine”. The information in the KB is obtained by interviewing people who are experts in the area in question.

Morphological Filtering Principles

2011 ◽  
pp. 1102-1111
Author(s):  
Jose Crespo
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Mathematical Morphology ◽  
Digital Form ◽  
Practical Applications ◽  
Theoretical Investigations ◽  
Morphological Filtering ◽  
The 1960S

In the last fifty years, approximately, advances in computers and the availability of images in digital form have made it possible to process and to analyze them in automatic (or semi-automatic) ways. Alongside with general signal processing, the discipline of image processing has acquired a great importance for practical applications as well as for theoretical investigations. Some general image processing references are (Castleman, 1979) (Rosenfeld & Kak, 1982) (Jain, 1989) (Pratt, 1991) (Haralick & Shapiro, 1992) (Russ, 2002) (Gonzalez & Woods, 2006). Mathematical Morphology, which was founded by Serra and Matheron in the 1960s, has distinguished itself from other types of image processing in the sense that, among other aspects, has focused on the importance of shapes. The principles of Mathematical Morphology can be found in numerous references such as (Serra, 1982) (Serra, 1988) (Giardina & Dougherty, 1988) (Schmitt & Mattioli, 1993) (Maragos & Schafer, 1990) (Heijmans, 1994) (Soille, 2003) (Dougherty & Lotufo, 2003) (Ronse, 2005).

Approximate Compressor-Based Multiplier Design Methodology for Error-Resilient Digital Signal Processing

2020 ◽  
Vol 29 (14) ◽  
pp. 2050233
Author(s):  
Zhixi Yang ◽  
Xianbin Li ◽  
Jun Yang
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Resource Utilization ◽  
Finite Impulse Response ◽  
Digital Signal ◽  
Fir Filter ◽  
Electrical Performance ◽  
Approximate Computing ◽  
Dsp Applications

As many digital signal processing (DSP) applications such as digital filtering are inherently error-tolerant, approximate computing has attracted significant attention. A multiplier is the fundamental component for DSP applications and takes up the most part of the resource utilization, namely power and area. A multiplier consists of partial product arrays (PPAs) and compressors are often used to reduce partial products (PPs) to generate the final product. Approximate computing has been studied as an innovative paradigm for reducing resource utilization for the DSP systems. In this paper, a 4:2 approximate compressor-based multiplier is studied. Approximate 4:2 compressors are designed with a practical design criterion, and an approximate multiplier that uses both truncation and the proposed compressors for PP reduction is subsequently designed. Different levels of truncation and approximate compression combination are studied for accuracy and electrical performance. A practical selection algorithm is then leveraged to identify the optimal combinations for multiplier designs with better performance in terms of both accuracy and electrical performance measurements. Two real case studies are performed, i.e., image processing and a finite impulse response (FIR) filter. The design proposed in this paper has achieved up to 16.96% and 20.81% savings on power and area with an average signal-to-noise ratio (SNR) larger than 25[Formula: see text]dB for image processing; similarly, with a decrease of 0.3[Formula: see text]dB in the output SNR, 12.22% and 30.05% savings on power and area have been achieved for an FIR filter compared to conventional multiplier designs.

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