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To further research the extracting method of the pig gait features, the paper firstly focus on the extraction of the pig gait frequency. A gait frequency extraction method was proposed based on analyzing the skeleton endpoints of depth image. Firstly, a series of processes, including skeleton extracting and pruning, were taken to the frames of depth image sequences. Secondly, a path similarity skeleton graph matching method was introduced to distinguish the fore-leg endpoints and the hind-leg endpoints from the skeleton graph. Then considering the characteristics of the depth image, a rule to distinguish the far-side endpoint and the near-side endpoint was constructed by calculating the average value of neighbor skeleton points of the endpoint. After ascertaining the skeleton endpoints of four legs, a variable was defined to represent the relative position of the far-side endpoint and the near-side endpoint, along the horizontal direction between frames in a sequence. Furthermore, the fitting sine curves were used to represent the variations of the fore-leg endpoints and the hind-leg endpoints separately. At last, the reciprocal of the fitting sine curve frequency can be calculated and the INTPART of double reciprocal was regarded as the fore-leg steps (FS) or the hind-leg steps (HS). The complete step (CS) was defined as the minimum of FS and HS. The finally gait frequency can be calculated by using the CS value to divide the duration of the sequence. To verify the proposal method, 28 depth image sequences of pig moving were acquired by using the KINECT depth camera, at the Rongxin pig farming of Danyang city in Jiangsu province, China. Another 28 sequences were achieved by mirror transforming along the horizontal direction to the native sequences. Experiments were taken for all the 56 sequences by using the proposal method. Experimental results show that the success rate of the method proposed in this paper is 82.1%, up to 92% for the situation when the pig moves continuously and the moving directions is perpendicular or nearly perpendicular to the axis of the depth camera only. Incorrect results often appear when the pig stays for a long time between steps or by non-cross steps, it needs to further adapt the proposal method. For the situation of rough variation of the pig body occurring in the sequence, the proposal method is not suited because the matching of skeleton points can not be achieved. That is the insufficient point of the proposal method. The proposal method would help to carry the further research of the abnormal gait of pig and construct the abnormal monitoring system by fusion of multi-source vision features. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Video analysis has been widely used to perceive the behavior of animals for precise dairy farming, which is useful to increase the productivity and reduce the disease rate. Using computer vision technique to recognize the individual cow is feasible to improve the efficiency of the automatic milking and feeding system. Effective and accurate recognition of individual dairy cattle is the prerequisite and foundation to record and analyze the animal behavior automatically. As the classic method of individual recognition, the typical electronic identification device, referred to a radio frequency identification device (RFID), must be installed on the neck or another position of the animal. But the available distance is limited and the RFID tags suffer from some shortages such as the loss of tags, tempering, and animal welfare. Besides, it requires extra device and redundant process to recognize the individual cow in a video using RFID method. Therefore, it is necessary to develop an accurate and efficient system for recognizing individual cows in feeding environment utilizing image processing method. In this paper, individual dairy cattle were recognized using the body images based on convolutional neural networks with video analysis method. Side-view images with a resolution of 704 pixels ×576 pixels were recorded when cows passed a narrow aisle to water trough. For target detecting, the frame difference method was implemented to obtain the outline and motion boundary of the cow. By dividing the target image into several same-width sections, the head and tail were removed from the image after checking the distribution of the target in the section. Because the ratio of the body's depth to cow's height was fixed at 0.6, the body area was located by drawing a box tangent to the back posture and then zoomed out 0.8 times of it to remove the external redundancy. For tracking the body image, template matching method was used to find the body area in the current frame by calculating the similarity against the target image in the previous frame. A convolutional neural network was built after analyzing the characteristics of the body images of cows. The network consisted of one input layer, two group of convolution-subsampling layers, and one output layer. The size of convolutional kernel was 5×5, and the subsampling size was 2×2. After testing different types of network architecture, the number of the feature maps in the first and third convolution layer were determined as 4 and 6, respectively, and the third convolution layers was partly connected to the second subsampling layer. The output layer was built up with 30 perceptrons, corresponding to the patterns of cows in the herd. After graying, resizing and normalizing, the body image of cow was transferred into a matrix sized 48×48 as the input of the network. 30 cows were captured 12 times for each, and 360 sets of videos were obtained in total, from which 60000 training frames, 21730 testing frames and 90 testing videos were selected randomly. In the tenth training epoch, the cost function was first less than 0.01. The result showed that 90.55% of the testing frames and 93.33% of the testing videos were recognized correctly, respectively. The testing data were captured from 7 a.m. to 6 p.m., so the network presented high robustness to the lightness diversity. The average elapsed time for recognizing one frame was lower than 0.01 s, and the total elapsed time for processing and recognizing one video was about 1 min, which showed a remarkable working efficiency and practicability. It suggested that the methods proposed here are feasible to recognize the individual dairy cattle. This study proves that the image processing technique has a great potential for recognition of animals. [ABSTRACT FROM AUTHOR]

The parameters such as the length, the number of ear rows, and the quantity of kernels in an ear of corn were measured during corn breeding and quality studies. It is usually done mainly manually. This research proposes an efficient image processing algorithm to detect the parameters of an ear of corn based on a machine vision. An experimental device was designed to detect the parameters. It mainly included a computer, a module of data acquisition and control, a stepper motor, a stepper motor driver, a PC camera, and other mechanical components. The computer was used to control the stepper motor to rotate the ear of corn and trigger the PC camera to capture images. The image was segmented after the ear of corn was captured. Its contour was traced. The length and the width of it were obtained by measuring the contour. The horizontal and vertical accumulated pixel values histograms were used in this research. One point in the upper edge and one point in the lower edge of the central ear row were found by first searching for the concaves of the horizontal accumulated pixel values histogram in a specified region. All the points in the upper and the lower edges of the central row were obtained by searching for the concaves of the horizontal accumulated pixel values histograms in a specified moving region which moved following the edge of the central ear row direction. So the image of this central ear row was determined. Each gap between the adjacent kernels could be distinguished by searching for the concaves of the vertical accumulated pixel values histogram in the image area of the central ear row. Then the width of the central ear row and the quantity of kernels in this ear row were recorded. The image of the next adjacent ear row was taken while this ear row was rotated to the location in which the former ear row was imaged. The condition of stopping detection was judged by matching the image of the current ear row with the first. So the number of the ear rows was determined. The quantity of the kernels in this ear of corn could be obtained by accumulating the kernels of all ear rows. In this research, an experimental device was designed to detect the parameters of an ear of corn. And an algorithm was supplied on the base of a machine vision for the same purpose. The image of each ear row in the ear of corn was effectively taken with no repeat. The parameters were detected such as the length and the width of the ear of corn, the width of one ear row, the number of ear rows, and the quantity of kernels in the ear of corn. Experiments showed that the measurement accuracy of the length, the width, and the number of the ear rows of the ear of corn was up to 98%. The measurement accuracy of the width of each ear row and the quantity of kernels was up to 95%. The detection speed was about 102 seconds per ear of corn. [ABSTRACT FROM AUTHOR]