Pose evaluation based on bayesian classification error

Pose evaluation is a fundamental issue in image processing and computer vision. In this paper, we propose a new method called BCE for pose evaluation based on Bayesian classification error. Various image cues are incorporated to depict an object including object shape, side region statistics and temporal information. Then a PEF (Pose Evaluation Function) is constructed based on Bayesian classification error, and an efficient algorithm to calculate it is developed. We test our new method with real outdoor image sequences, and use two criteria to compare it with two other representative ones. It is shown that our new method leads to better performance with respect to localization accuracy and robustness against general clutter and occlusion.

Modelling the Effects of Parking Charge and Supply Policy Using System Dynamics Method

Reasonable parking charge and supply policy are essential for the regular operation of the traffic in city center. This paper develops an evaluation model for parking policies using system dynamics. A quantitative study is conducted to examine the effects of parking charge and supply policy on traffic speed. The model, which is composed of three interrelated subsystems, first summarizes the travel cost of each travel mode and then calibrates the travel choice model through the travel mode subsystem. Finally, the subsystem that evaluates the state of traffic forecasts future car speed based on bureau of public roads (BPR) function and generates new travel cost until the entire model reaches a steady state. The accuracy of the model is verified in Hangzhou Wulin business district. The related error of predicted speed is only 2.2%. The results indicate that the regular pattern of traffic speed and parking charge can be illustrated using the proposed model based on system dynamics, and the model infers that reducing the parking supply in core area will increase its congestion level and, under certain parking supply conditions, there exists an interval of possible pricing at which the service reaches a level that is fairly stable

Abnormal Identification of Swine Flu Clinical Characteristics Based on Body Temperature and Behavior

International audienceThe pathology and virus isolation are the mainly diagnostic approaches for swine flu currently. Although the diagnosis rate is high, it is not conducive to detecting and intervening the infected pigs timely because of the serious time delay. Therefore, this paper proposed a novel framework method for the early detection and warned of swine influenza. The Jilin landrace are as the subjects of the experiment in this paper. Firstly, the body temperature changes were monitored compared between healthy and infected pigs respectively. And then the machine vision method was used to identify the basic pre-defined behavior of the landrace. Afterward, the behavior of the healthy and infected pigs were determined the abnormality or not. In the experiments, the results showed that the temperature of the infected pigs increased from 1–2 h to 40.3–41.5 ℃ and the lying status of the sick pigs was significantly increased compared to other activities such as feeding and drinking water. The experimental results showed that this method was effective for early detection of swine flu

The Diagnostic Value of Serum Gastrin-17 and Pepsinogen for Gastric Cancer Screening in Eastern China

Objective. To evaluate the diagnostic value of gastrin-17 (G-17) and pepsinogen (PG) in gastric cancer (GC) screening in China, especially eastern China, and to determine the best diagnostic combination and threshold (cutoff values) to screen out patients who need gastroscopy. Methods. The serum concentrations of G-17 and pepsinogen I and II (PGI and PGII) in 834 patients were analyzed, and the PGI/PGII ratio (PGR) was calculated. According to pathological results, patients can be divided into chronic nonatrophic gastritis (NAG)/chronic atrophic gastritis (CAG)/intraepithelial neoplasia (IN)/GC groups. The differences in G-17, PG, and PGR in each group were analyzed, and their values in GC diagnosis were evaluated separately and in combination. Results. There were differences in serum G-17, PGII, and PGR among the four groups (NAG/CAG/IN/GC) (P≤0.001). In total, 54 GC cases were diagnosed, of which 50% were early GC. There was no significant difference in the PGI levels among the four groups (P=0.377). NAG and CAG composed the chronic gastritis (CG) group. The G-17 and PGII levels in the IN and GC groups were higher than those in the CG group (both P≤oth C), while the PGR levels were lower (P≤lower). When distinguishing NAG from CAG, the best cutoff value for G-17 was 9.25 pmol/L, PGII was 7.06 μg/L, and PGR was 12.07. When distinguishing CG from IN, the best cutoff value for G-17 was 3.86 pmol/L, PGII was 11.92 μg/L, and PGR was 8.26. When distinguishing CG from GC, the best cutoff value for G-17 was 3.89 pmol/L, PGII was 9.16 μg/L, and PGR was 14.14. The sensitivity, specificity, accuracy, and positive and negative predictive values of G-17/PGII/PGR for GC diagnosis were 83.3%/70.4%/79.6%, 51.8%/56.3%/47.8%, 53.8%/57.2%/49.9%, 10.7%/10.9%/9.6%, and 97.8%/96.5%/97.1%, respectively. The sensitivity, specificity, accuracy, and positive predictive and negative predictive values of PGII/G-17 vs. PGR/G-17 vs. PGR/PGII in the diagnosis of GC were 63.0% vs. 70.4% vs. 64.8%, 70.5% vs. 70.1% vs. 60.4%, 70.0% vs. 70.1% vs. 60.7%, 12.9% vs. 14.0% vs. 10.2%, and 96.5% vs. 97.2% vs. 96.1%, respectively. Conclusion. The PGII and G-17 levels in patients with gastric IN and GC were significantly increased, while the serum PGR level was significantly decreased. Serological detection is effective for screening GC. The combination of different markers can improve the diagnostic efficiency. The highest diagnostic accuracy was G-17 combined with PGR, and the best cutoff values were G−17>3.89 pmol/L and PGR<14.14