Caenorhabditis elegans (C. elegans) is a popular and excellent model for studies of aging due to its short lifespan. Methods for precisely measuring the physiological age of C. elegans are critically needed, especially for antiaging drug screening and genetic screening studies. The effects of various antiaging interventions on the rate of aging in the early stage of the aging process can be determined based on the quantification of physiological age. However, in general, the age of C. elegans is evaluated via human visual inspection of morphological changes based on personal experience and subjective judgment. For example, the rate of motor activity decay has been used to predict lifespan in early- to mid-stage aging. Using image processing, the physiological age of C. elegans can be measured and then classified into periods or classes from childhood to elderhood (e.g., 3 periods comprising days 0-2, 4-6 and 10-12) by using texture entropy (Shamir, L. et al., 2009). Our dataset consists of 913 microscopic images of C. elegans, with approximately 60 images per day from day 1 to day 14 of adulthood. We present quantitative methods to measure the physiological age of C. elegans with convolution neural networks (CNNs), which can measure age with a granularity of days rather than periods. The methods achieved a mean absolute error (MAE) of less than 1 day for the measured age of C. elegans. In our experiments, we found that after training and testing our dataset, 5 popular CNN models, 50-layer residual network (ResNet50), InceptionV3, InceptionResNetV2, 16-layer Visual Geometry Group network (VGG16) and MobileNet, measured the physiological age of C. elegans with an average testing MAE of 1.58 days. Furthermore, based on the results, we propose two models, one model for linear regression analysis and the other model for logistic regression, that combine a CNN model and a new attribute: curved_or_straight. The linear regression analysis model achieved a test MAE of 0.94 days; the logistic regression model achieved an accuracy of 84.78 percent with an error tolerance of 1 day.

Caenorhabditis elegans (C. elegans) is a kind of transparent nematode with the body length about 1 mm. C. elegans has been widely used in the field of modern biology for studying organs and cells, where a variety of physiological changes about the whole life of C. elegans are usually evaluated via human visual inspection. In order to facilitate the automatically quantitative evaluation with computer, the segmentation of C. elegans from microscopic image is a primary and significant task. In this paper, a method including image transformation, line thresholding and morphological operations is presented to segment the C. elegans from a microscopic image. Experimental results confirm the feasibility of the proposed method and some future works are discussed.

The increasing popularity of the internet means that digital multimedia are transmitted more rapidly and easily. And people are very aware for media ownership. However, digital watermarking is an efficient and promising means to protect intellectual properties. Based on the intellectual property attention in the information era, how to protect the personal ownership is extremely important and a necessary scheme. In this paper, we propose an effective video watermarking method based on a pseudo-3-D discrete cosine transform (DCT) and quantization index modulation (QIM) against several attacks. The watermark is mainly inserted into the uncompressed domain by adjusting the correlation between DCT coefficients of the selected blocks, and the watermark extraction is blind. This approach consists of a pseudo-3-D DCT, watermark embedding, and extraction. A pseudo-3-D DCT, which is taken DCT transformation twice, will be first utilized to calculate the embedding factor and to obtain the useful messages. Using the QIM, we embed the watermark into the quantization regions from the successive raw frames in the uncompressed domain and record the relative information to create a secret embedding key. This secret embedding key will further apply to extraction. Experimental results demonstrate that the proposed method can survive filtering, compressions, luminance change, and noise attacks with a good invisibility and robustness.

In this paper, we propose an approach to categorize the film classes by using low-level features and visual features. The goal of this approach is to classify the films into genres. Our current domain of study is using the movie preview. A film preview often emphasizes the theme of a film and hence provides suitable information for classification process. In our approach, we classify films into three broad categories: action, dramas, and thriller films. Four computable video features (average shot length, color variance, motion content and lighting key) and visual effects are combined in our approach to provide the advantage information to demonstrate the movie category. Our approach can also be extended for other potential applications, including the browsing and retrieval of videos on the Internet, video-on-demand, and video libraries.

There is a substantial interest in locating bookbacks in a bookrack image using the edge information contained in the image. The effective edge information considered here is defined between two adjacent bookbacks, called a "bookback edge". The main steps of the proposed approach are as follows: the edge points of the input image are first detected by a simple thresholding method, a measure algorithm applied to the edge information locate all possible bookback edges, and the final bookback edges are obtained by averaging the fuzzy bookback edges. The experiments show that the proposed approach is feasible.

Colors in a scene change under different illuminants. By adopting models that are used to describe human color constancy, the maximum-spectral-value method is proposed to estimate the illuminant from the maximum distribution of reflected lights in an image. From the experimental results, the proposed method recovers colors well with different illuminants.

An automatic histogram specification method is proposed for image enhancement. Parameterized fuzzy number (PFN) is adopted for the representation of image histogram. The desired histogram PFN is automatically constructed by the fuzzy addition of the tuning PFN and the original one. The tuning PFN is implied from the fuzzy inference rules. The mapping function is decided by the relations between the desired PFN and the original one. The effectual results are demonstrated by the mapping functions which meet the image enhancement requirements. The performance and effectiveness are shown by the analysis and the resultant image in comparison with other methods.>

An algorithm for achieving color constancy is presented. The algorithm consists of four models: the finite-dimensional model, the homomorphic model, the statistical model, and the recovery model. From them, it is possible to estimate the surface reflectance, even when the spectral distribution of the ambient light is unknown. Thus color constancy can be achieved under biased illumination. To prove the correctness of the authors' algorithm, some experiments are conducted under different illuminative conditions.>

An automatic approach used for making a human face model is proposed. It is a system with input forms of a 2.5D range data set and a 2D color image. The information obtained from the range data set includes the anatomical sites of features and the geometrical data of face. The information extracted from the 2D color image includes the boundaries of facial features and the attributes of facial textures. These two sources are integrated to form a volumetric facial model.>

The authors propose parallel implementation of prime-factor discrete cosine transform (DCT) on the orthogonal-multiprocessor (OMP) architecture when the transform size N can be decomposed into two mutually prime members N=N/sub 1/N/sub 2/. The implementation shows that the existing prime-factor DCT algorithm can be mapped easily on the OMP architecture without modification. The proposed algorithms include input index mapping, summation, scaling, adjust rotation, and output index mapping. The time complexity of the algorithm is O(N/sub 1/+N/sub 2/) on a J processor OMP, where J is the maximum dimension of Winograd-Hartley scaling matrices.>

This paper describes typical research on Chinese optical character recognition in Taiwan. Chinese characters can be represented by a set of basic line segments called strokes. Several approaches to the recognition of handwritten Chinese characters by stroke analysis are described here. A typical optical character recognition (OCR) system consists of four main parts: image preprocessing, feature extraction, radical extraction and matching. Image preprocessing is used to provide the suitable format for data processing. Feature extraction is used to extract stable features from the Chinese character. Radical extraction is used to decompose the Chinese character into radicals. Finally, matching is used to recognize the Chinese character. The reasons for using strokes as the features for Chinese character recognition are the following. First, all Chinese characters can be represented by a combination of strokes. Second, the algorithms developed under the concept of strokes do not have to be modified when the number of characters increases. Therefore, the algorithms described in this paper are suitable for recognizing large sets of Chinese characters.

The authors review their pseudo-one-subcycle parallel thinning algorithm. They present two improved versions of this algorithm, and describe a two-stage structure to realize the one-subcycle parallel algorithm. The first stage is to produce a perfect 8-curve excluding T-junction thin line. The second is to obtain the isotropic skeleton of an L-shaped pattern. The two stage structure consists of a thinning table and a control unit. The thinning table is used to provide the attributions for an input 3*3 local pattern. The control unit is used to check the removal of the center pixel of this local pattern, and the inputs of the control unit also comprise the outputs of other neighboring thinning tables. This structure can exactly implement the proposed one-subcycle parallel algorithms. The two improved algorithms have been implemented. Experiments confirm that the improved algorithms can produce the desired effective thin line and also show that the structure realized is feasible and practicable.>

Chinese characters are mapped from the spatial domain into the parametric one for stroke extraction, and the dynamic programming matching (DP matching) algorithm is applied to recognize Chinese characters. This method overcomes not only the problem of noise sensitivity in the local feature approach, but also the problem of being time consuming in the global feature approach. Results are presented for two experiments conducted for a database called ETL8, which contains 881 Chinese characters and 160 variations for each one, to prove the usefulness of the MHT and DP matching methods. An actual recognition rate of 94.5% was obtained for 351 Chinese characters in the ETL8.>

This paper describes a new approach to the recognition of multi-font printed Chinese characters. The basic idea is to encode a character in terms of two pre-defined stroke relations, namely, relative position relation and relative direction relation. The code-mapping method chosen in our system possesses two main advantages: the first is that the tree-like data base can be easily extended, and the second is that the processing time is independent of the amount of data base. Since the stability of the extracted strokes greatly affects the coding results, a new stroke merging method, which has been experimentally proven to extract strokes more steadily, is also proposed.