Text Extraction From Natural Scene: Methodology And Application

With the popularity of the Internet and the smart mobile device, there is an increasing demand for the techniques and applications of image/video-based analytics and information retrieval. Most of these applications can benefit from text information extraction in natural scene. However, scene text extraction is a challenging problem to be solved, due to cluttered background of natural scene and multiple patterns of scene text itself. To solve these problems, this dissertation proposes a framework of scene text extraction. Scene text extraction in our framework is divided into two components, detection and recognition. Scene text detection is to find out the regions containing text from camera captured images/videos. Text layout analysis based on gradient and color analysis is performed to extract candidates of text strings from cluttered background in natural scene. Then text structural analysis is performed to design effective text structural features for distinguishing text from non-text outliers among the candidates of text strings. Scene text recognition is to transform image-based text in detected regions into readable text codes. The most basic and significant step in text recognition is scene text character (STC) prediction, which is multi-class classification among a set of text character categories. We design robust and discriminative feature representations for STC structure, by integrating multiple feature descriptors, coding/pooling schemes, and learning models. Experimental results in benchmark datasets demonstrate the effectiveness and robustness of our proposed framework, which obtains better performance than previously published methods. Our proposed scene text extraction framework is applied to 4 scenarios, 1) reading print labels in grocery package for hand-held object recognition; 2) combining with car detection to localize license plate in camera captured natural scene image; 3) reading indicative signage for assistant navigation in indoor environments; and 4) combining with object tracking to perform scene text extraction in video-based natural scene. The proposed prototype systems and associated evaluation results show that our framework is able to solve the challenges in real applications

Effects of zinc sources and levels of zinc amino acid complex on growth performance, hematological and biochemical parameters in weanling pigs

Objective The objective of the study was to investigate the effects of zinc amino acid complex (ZnAA) on growth performance, hematological and biochemical parameters in weanling pigs. Methods In Exp. 1, a total of 216 Duroc×Landrace×Large White weanling pigs were assigned randomly to 6 dietary treatments. Each treatment had 6 replicates (pens) with 6 pigs each. The diets were corn-soybean meal based with supplementation of 0, 20, 40, 80, 120 mg Zn/kg from ZnAA or 40 mg Zn/kg from feed-grade zinc sulfate. The experiment lasted 42 days. In Exp. 2, a total of 180 weanling pigs were assigned randomly to 3 dietary treatments supplemented with 0, 80, or 800 mg Zn/kg from ZnAA. Results In Exp. 1, pigs fed 40 to 80 mg Zn/kg from ZnAA had higher (p<0.05) average daily gain (ADG) than the unsupplemented group during d 0 to 14. During d 0 to 42, the pigs fed 20 to 120 mg Zn/kg from ZnAA had increased (p<0.05) ADG. Pigs fed 20 to 120 mg/kg Zn from ZnAA had lower feed:gain (p<0.05), increased the activity of serum Cu-Zn superoxide dismutase on d 14, and increased serum Zn levels on d 42 (p<0.05). In Exp. 2, pigs fed diets with 800 mg Zn/kg had increased average daily feed intake during d 15 to 28 (p<0.05) compared to the unsupplemented group. During d 0 to 28, the pigs fed supplemental Zn had increased ADG (p<0.05). On d 14 and d 28, pigs fed supplemental Zn had higher the serum alkaline phosphatase activities (p<0.05). No significant differences were observed in the hematological parameters and organ indices. Conclusion Supplementation with 20 to 80 mg/kg Zn from ZnAA improved the growth performance in weaned pigs. The piglets can tolerate up to 800 mg/kg Zn from ZnAA with limited potential health effects

Air-Backed Aluminum Shells Subjected to Underwater Penetration: Torpedo Interception Simulations

Underwater torpedoes have become a serious threat to ocean liners and warships, and the interception against attacking torpedoes is always the hotspot in marine engineering. To simulate the underwater torpedo interception by a high velocity projectile, this work numerically deals with the process of projectile water entry and sequent penetration into underwater aluminum shells, whereby conical and ogival nose projectiles are comparatively studied. With the arbitrary Lagrange&ndash;Euler (ALE) algorithm adopted to describe fluid medium, the projectile water entry model is developed and validated against the test data. Similarly, the penetration model validation is made by modeling a tungsten ball perforation on an aluminum plate. Covered by water fluid, the air-backed aluminum shell is utilized to simulate an underwater torpedo subjected to projectile impact. The numerical predictions of underwater penetration reveal that ogival nose projectiles have a superior performance in underwater motion and perforation while conical nose counterparts deteriorate the shell targets more severely. For 20 cm, 40 cm and 60 cm underwater depth scenarios, a numerical prediction suggests that the energy consumed by water is proportional to the water depth, meanwhile aluminum shell perforation absorbs almost the identical projectile kinetic energy. Such findings may shed some light on the nose shape optimization design of high velocity projectile intercepting underwater torpedoes