Koszul differential graded algebras and BGG correspondence

The concept of Koszul differential graded algebra (Koszul DG algebra) is introduced. Koszul DG algebras exist extensively, and have nice properties similar to the classic Koszul algebras. A DG version of the Koszul duality is proved. When the Koszul DG algebra $A$ is AS-regular, the Ext-algebra $E$ of $A$ is Frobenius. In this case, similar to the classical BGG correspondence, there is an equivalence between the stable category of finitely generated left $E$-modules, and the quotient triangulated category of the full triangulated subcategory of the derived category of right DG $A$-modules consisting of all compact DG modules modulo the full triangulated subcategory consisting of all the right DG modules with finite dimensional cohomology. The classical BGG correspondence can derived from the DG version.Comment: 29 page

DIY laboratories and business innovation ecosystems: The case of pharmaceutical industry

This paper conducts an embedded case study to verify a conceptual framework by which biopharma research in Do-It-Yourself (DIY) laboratories can be integrated into Research and Development (R&D) networks of the pharmaceutical industry. As an early attempt to extend the perspective of business innovation ecosystem into the research on DIY laboratories, this study reveals three major findings. First, DIY laboratories, contract research organizations (CROs) and pharmaceutical firms interdependently position and link with each other in an innovation ecosystem for new drug development. Second, through properly managing the issues of resource utilization and innovation appropriability, CROs play important hub and knowledge broker roles in coordinating and aligning different priorities and expectations of the key players in this innovation ecosystem. Third, this study maps and verifies two knowledge transfer models through which novel research findings in DIY laboratories can be converted into real commercial returns

Segmenting characters from license plate images with little prior knowledge

In this paper, to enable a fast and robust system for automatically recognizing license plates with various appearances, new and simple but efficient algorithms are developed to segment characters from extracted license plate images. Our goal is to segment characters properly from a license plate image region. Different from existing methods for segmenting degraded machine-printed characters, our algorithms are based on very weak assumptions and use no prior knowledge about the format of the plates, in order for them to be applicable to wider applications. Experimental results demonstrate promising efficiency and flexibility of the proposed scheme. © 2010 IEEE

Dense feature correspondence for video-based endoscope three-dimensional motion tracking

This paper presents an improved video-based endoscope tracking approach on the basis of dense feature correspondence. Currently video-based methods often fail to track the endoscope motion due to low-quality endoscopic video images. To address such failure, we use image texture information to boost the tracking performance. A local image descriptor - DAISY is introduced to efficiently detect dense texture or feature information from endoscopic images. After these dense feature correspondence, we compute relative motion parameters between the previous and current endoscopic images in terms of epipolar geometric analysis. By initializing with the relative motion information, we perform 2-D/3-D or video-volume registration and determine the current endoscope pose information with six degrees of freedom (6DoF) position and orientation parameters. We evaluate our method on clinical datasets. Experimental results demonstrate that our proposed method outperforms state-of-the-art approaches. The tracking error was significantly reduced from 7.77 mm to 4.78 mm. © 2014 IEEE

ECCH: A novel color coocurrence histogram

In this paper, a novel color cooccurrence histogram method, named eCCH which stands for color cooccurrence histogram at edge points, is proposed to describe the spatial-color joint distribution of images. Unlike all existing ideas, we only investigate the color distribution of pixels located at the two sides of edge points on gradient direction lines. When measuring the similarity of two eCCHs, the Gaussian weighted histogram intersection method is adopted, where both identical and similar color pairs are considered to compensate color variations. Comparative experimental results demonstrate the performance of the proposed eCCH in terms of robustness to color variance and small computational complexity. ©2010 IEEE

A triple-diagonal gradient-based edge detection

Gradient-based edge detection is a straightforward method to identity the edge points in the original grey-level image. It is consistent with the intuition that in the human vision system the edge points always appear where the change of grey-level is greatest within their neighbourhood. In this paper, triple-diagonal gradient-based edge detection is introduced. It is based on the features of Spiral Architecture and computes the gradients in three diagonal directions instead of approximating the gradient in one direction only as the traditional methods do. Essentially, it improves the accuracy for locating edge points. As a result, it does not need any supplementary processing to enhance the edge map

Symmetric color ratio in spiral architecture

Color ratio gradient (CRG) is a robust method used for color image retrieval and object recognition. It has been proven to be illumination-independent and geometry-insensitive when tested on scenery images. However, the color ratio gradient produces unsatisfying matching results when dealing with an object which appears rotated by a certain relative angle in the model and target images. In this paper, we adopt the idea of color ratio gradient and develop a new method called Symmetric Color Ratio (SCR) based on a hexagonal image structure, the Spiral Architecture (SA). We focus on license plate images and our aim is to achieve a higher matching rate between the SCR histogram of the images within same class in order to separate different classes of images. Our experimental results demonstrate that the proposed SCR is robust to changes over view angles. © Springer-Verlag Berlin Heidelberg 2006

A fast algorithm for license plate detection in various conditions

This paper proposes a fast algorithm detecting license plates in various conditions. There are three main contributions in this paper. The first contribution is that we define a new vertical edge map, with which the license plate detection algorithm is extremely fast. The second contribution is that we construct a cascade classifier which is composed of two kinds of classifiers. The classifiers based on statistical features decrease the complexity of the system. They are followed by the classifiers based on Haar-features, which make it possible to detect license plate in various conditions. Our algorithm is robust to the variance of the illumination, view angle, the position, size and color of the license plates when working in complex environment. The third contribution is that we experimentally analyze the relations of the scaling factor with detection rate and processing time. On the basis of the analysis, we select the optimal scaling factor in our algorithm. In the experiments, both high detection rate (with low false positive rate) and high speed are achieved when the algorithm is used to detect license plates in various complex conditions. © 2006 IEEE