Abstract: For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.
Abstract: In this paper, we propose an efficient hierarchical DNA
sequence search method to improve the search speed while the
accuracy is being kept constant. For a given query DNA sequence,
firstly, a fast local search method using histogram features is used as a
filtering mechanism before scanning the sequences in the database.
An overlapping processing is newly added to improve the robustness
of the algorithm. A large number of DNA sequences with low
similarity will be excluded for latter searching. The Smith-Waterman
algorithm is then applied to each remainder sequences. Experimental
results using GenBank sequence data show the proposed method
combining histogram information and Smith-Waterman algorithm is
more efficient for DNA sequence search.
Abstract: In this paper, we propose a novel fast search algorithm for short MPEG video clips from video database. This algorithm is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of the frame image. Instead of fully decompressed video frames, partially decoded data, namely DC images are utilized. Combined with active search [4], a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 MPEG video clips which each length is 15 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 80ms, and Equal Error Rate (ERR) of 3 % is achieved, which is more accurately and robust than conventional fast video search algorithm.
Abstract: In this paper, we present an improved fast and robust
search algorithm for copy detection using histogram-based features for
short MPEG video clips from large video database. There are two
types of histogram features used to generate more robust features. The
first one is based on the adjacent pixel intensity difference quantization
(APIDQ) algorithm, which had been reliably applied to human face
recognition previously. An APIDQ histogram is utilized as the feature
vector of the frame image. Another one is ordinal histogram feature
which is robust to color distortion. Furthermore, by Combining with a
temporal division method, the spatial and temporal features of the
video sequence are integrated to realize fast and robust video search
for copy detection. Experimental results show the proposed algorithm
can detect the similar video clip more accurately and robust than
conventional fast video search algorithm.
Abstract: In this paper, we propose an improved fast search
algorithm using combined histogram features and temporal division
method for short MPEG video clips from large video database. There
are two types of histogram features used to generate more robust
features. The first one is based on the adjacent pixel intensity
difference quantization (APIDQ) algorithm, which had been reliably
applied to human face recognition previously. An APIDQ histogram is
utilized as the feature vector of the frame image. Another one is
ordinal feature which is robust to color distortion. Combined with
active search [4], a temporal pruning algorithm, fast and robust video
search can be realized. The proposed search algorithm has been
evaluated by 6 hours of video to search for given 200 MPEG video
clips which each length is 30 seconds. Experimental results show the
proposed algorithm can detect the similar video clip in merely 120ms,
and Equal Error Rate (ERR) of 1% is achieved, which is more
accurately and robust than conventional fast video search algorithm.
Abstract: A hybrid feature based adaptive particle filter algorithm is presented for object tracking in real scenarios with static camera.
The hybrid feature is combined by two effective features: the Grayscale Arranging Pairs (GAP) feature and the color histogram feature. The GAP feature has high discriminative ability even under conditions of severe illumination variation and dynamic background
elements, while the color histogram feature has high reliability to identify the detected objects. The combination of two features covers the shortage of single feature. Furthermore, we adopt an updating
target model so that some external problems such as visual angles can be overcame well. An automatic initialization algorithm is introduced which provides precise initial positions of objects. The experimental
results show the good performance of the proposed method.