Veni Vidi Vici, A Three-Phase Scenario For Parameter Space Analysis in Image Analysis and Visualization

Automatic analysis of the enormous sets of images is a critical task in life sciences. This faces many challenges such as: algorithms are highly parameterized, significant human input is intertwined, and lacking a standard meta-visualization approach. This paper proposes an alternative iterative approach for optimizing input parameters, saving time by minimizing the user involvement, and allowing for understanding the workflow of algorithms and discovering new ones. The main focus is on developing an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. This technique is implemented as a prototype called Veni Vidi Vici, or "I came, I saw, I conquered." This strategy is inspired by the mathematical formulas of numbering computable functions and is developed atop ImageJ, a scientific image processing program. A case study is presented to investigate the proposed framework. Finally, the paper explores some potential future issues in the application of the proposed approach in parameter space analysis in visualization

Simulated Hough Transform Model Optimized for Straight-Line Recognition Using Frontier FPGA Devices

The use of the Hough transforms to identify shapes or images has been extensively studied in the past using software for artificial intelligence applications. In this article, we present a generalization of the goal of shape recognition using the Hough transform, applied to a broader range of real problems. A software simulator was developed to generate input patterns (straight-lines) and test the ability of a generic low-latency system to identify these lines: first in a clean environment with no other inputs and then looking for the same lines as ambient background noise increases. In particular, the paper presents a study to optimize the implementation of the Hough transform algorithm in programmable digital devices, such as FPGAs. We investigated the ability of the Hough transform to discriminate straight-lines within a vast bundle of random lines, emulating a noisy environment. In more detail, the study follows an extensive investigation we recently conducted to recognize tracks of ionizing particles in high-energy physics. In this field, the lines can represent the trajectories of particles that must be immediately recognized as they are created in a particle detector. The main advantage of using FPGAs over any other component is their speed and low latency to investigate pattern recognition problems in a noisy environment. In fact, FPGAs guarantee a latency that increases linearly with the incoming data, while other solutions increase latency times more quickly. Furthermore, HT inherently adapts to incomplete input data sets, especially if resolutions are limited. Hence, an FPGA system that implements HT is inefficient for small sets of input data but becomes more cost-effective as the size of the input data increases. The document first presents an example that uses a large Accumulator consisting of 1100 x 600 Bins and several sets of input data to validate the Hough transform algorithm as random noise increases to 80% of input data. Then, a more specifically dedicated input set was chosen to emulate a real situation where a Xilinx UltraScale+ was to be used as the final target device. Thus, we have reduced the Accumulator to 280 x  280 Bins using a clock signal at 250 MHz and a few tens input points. Under these conditions, the behavior of the firmware matched the software simulations, confirming the feasibility of the HT implementation on FPGA

An Image Understanding System for Detecting Indoor Features

The capability of identifying physical structures of an unknown environment is very important for vision based robot navigation and scene understanding. Among physical structures in indoor environments, corridor lines and doors are important visual landmarks for robot navigation since they show the topological structure in an indoor environment and establish connections among the different places or regions in the indoor environment. Furthermore, they provide clues for understanding the image. In this thesis, I present two algorithms to detect the vanishing point, corridor lines, and doors respectively using a single digital video camera. In both algorithms, we utilize a hypothesis generation and verification method to detect corridor and door structures using low level linear features. The proposed method consists of low, intermediate, and high level processing stages which correspond to the extraction of low level features, the formation of hypotheses, and verification of the hypotheses via seeking evidence actively. In particular, we extend this single-pass framework by employing a feedback strategy for more robust hypothesis generation and verification. We demonstrate the robustness of the proposed methods on a large number of real video images in a variety of corridor environments, with image acquisitions under different illumination and reflection conditions, with different moving speeds, and with different viewpoints of the camera. Experimental results performed on the corridor line detection algorithm validate that the method can detect corridor line locations in the presence of many spurious line features about one second. Experimental results carried on the door detection algorithm show that the system can detect visually important doors in an image with a very high accuracy rate when a robot navigates along a corridor environment

Computer-assisted detection of lung cancer nudules in medical chest X-rays

Diagnostic medicine was revolutionized in 1895 with Rontgen's discovery of x-rays. X-ray photography has played a very prominent role in diagnostics of all kinds since then and continues to do so. It is true that more sophisticated and successful medical imaging systems are available. These include Magnetic Resonance Imaging (MRI), Computerized Tomography (CT) and Positron Emission Tomography (PET). However, the hardware instalment and operation costs of these systems remain considerably higher than x-ray systems. Conventional x-ray photography also has the advantage of producing an image in significantly less time than MRI, CT and PET. X-ray photography is still used extensively, especially in third world countries. The routine diagnostic tool for chest complaints is the x-ray. Lung cancer may be diagnosed by the identification of a lung cancer nodule in a chest x-ray. The cure of lung cancer depends upon detection and diagnosis at an early stage. Presently the five-year survival rate of lung cancer patients is approximately 10%. If lung cancer can be detected when the tumour is still small and localized, the five-year survival rate increases to about 40%. However, currently only 20% of lung cancer cases are diagnosed at this early stage. Giger et al wrote that "detection and diagnosis of cancerous lung nodules in chest radiographs are among the most important and difficult tasks performed by radiologists"

Fast algorithm for real-time rings reconstruction

The GAP project is dedicated to study the application of GPU in several contexts in which real-time response is important to take decisions. The definition of real-time depends on the application under study, ranging from answer time of μs up to several hours in case of very computing intensive task. During this conference we presented our work in low level triggers [1] [2] and high level triggers [3] in high energy physics experiments, and specific application for nuclear magnetic resonance (NMR) [4] [5] and cone-beam CT [6]. Apart from the study of dedicated solution to decrease the latency due to data transport and preparation, the computing algorithms play an essential role in any GPU application. In this contribution, we show an original algorithm developed for triggers application, to accelerate the ring reconstruction in RICH detector when it is not possible to have seeds for reconstruction from external trackers

Optimization of manipulation logistics using data matrix codes

In the paper we deal with optimization of manipulation logistics using Data Matrix codes. Our goal is scanning and decoding Data Matrix codes in real-time. We have designed and verified an efficient computer aided method for location of the Data Matrix codes. This method is also suited to real-time processing and has been verified on a test set of images taken from real industrial world. We have proposed a modified, computationally efficient local thresholding technique that uses local mean and variation under the sliding window. The proposed Data Matrix code localization algorithm utilizes the connecting of the adjoining points into the continuous regions and determining of the boundaries of the outer region and it works in two basic steps: localization of the Finder Pattern and verification of the Timing Pattern. Part of the algorithm deals also with the decoding of the Data Matrix code using external libraries. Data Matrix codes can be used to mark logistic units, parts, warehousing positions, but also for automated robot navigation. Because of their low cost, accuracy, speed, reliability, flexibility and efficiency, as well as the ability to write large amounts of data on a small area, they still have a great advantage in logistics.[KEGA MS SR 003TU Z-4/2016

A New Weighted Region-based Hough Transform Algorithm for Robust Line Detection in Poor Quality Images of 2D Lattices of Rectangular Objects

In this work we present a novel kernel-based Hough Transform method for robust line detection in poor quality images of 2D lattices of rectangular objects. Following a preprocessing stage that specifies the connected regions of the image, the proposed method uses a kernel to specify each region's voting strength based on the following shape descriptors: a) its rectangularity, b) the orientation of the major side of its minimum area bounding rectangle (MBR), and c) the MBR's geometrical center. Experimental and theoretical analysis on the uncertainties associated with the geometrical center as well as the polar parameters of the MBR's major axis line equation allows for automatic selection of the parameters used to specify the shape of the kernel's footstep on the accumulator array. Comparisons performed on images of building facades taken under impaired visual conditions or with low accuracy sensors (e.g. thermal images) between the proposed method and other Hough Transform algorithms, show an improved accuracy of our method in detecting lines and/or linear formations. Finally, the robustness of the proposed method is shown in two other application domains those of, façade image rectification and skew detection and correction in rotated scanned documents