This paper introduces a laser scanner based measurement system for measuring crop/tree geometric characteristics. The measurement system, which is mounted on a Unmanned Ground Vehicle (UGV), contains a SICK LMS511 PRO laser scanner, a GPS, and a computer. The LMS511 PRO scans objects within distance up to 80 meters with a scanning frequency of 25 up to 100Hz and with an angular resolution of 0.1667° up to 1°. With an Ethernet connection, this scanner can output the measured values in real time. The UGV is a WIFI based remotely controlled agricultural robotics system. During field tests, the laser scanner was mounted on the UGV vertically to scan crops or trees. The UGV moved along the row direction with certain average travel speed. The experimental results show that the UGV's travel speed significantly affects the measurement accuracy. A slower speed produces more accurate measuring results. With the developed measurement system, crop/tree canopy height, width, and volume can be accurately measured in a real-time manner. With a higher spatial resolution, the original data set may even provide useful information in predicting crop/tree growth and productivity. In summary, the UGV based measurement system developed in this research can measure the crop/tree geometric characteristics with good accuracy and will work as a step stone for our future UGV based intelligent agriculture system, which will include variable rate spray and crop/tree growth and productivity prediction through analyzing the measured results of the laser scanner system.
In this paper, we present a novel approach on image object removal by extending subpatch texture synthesis
technique into redundant wavelet transform (RDWT) domain. As an overcompleted wavelet transform, RDWT
is shift invariant and obtained without downsampling. Also, each RDWT highpass subband exhibits one specific
orientation features of the image, in horizontal, vertical, or diagonal. All these make RDWT ideal for performing
texture synthesis object removal techniques. In our experiments, subpatch texture synthesis in RDWT is
introduced to remove unwanted objects from digital photographs. Specifically, for each RDWT subband, depending
on the subband orientation, a particular direction subpatch texture synthesis is applied independently.
Experimental results reveal that our simple algorithm performs better than previous methods.
In this paper, different types of mutihypothesis techniques are studied. Based on the transform domain multihypothesis provided in redundant discrete wavelet transform (RDWT), other multihypothesis methods, such as spatial domain or temporal domain multihypothesis are added to improve the performance of a single multihypothesis scheme. Experimental results show that combining two types of multihypothesis motion compensation (MHMC) is promising. Choosing wisely, combining three MHMC methods is possible to get a gain. But as more and more multihypothesis involved, the room of improvement
is getting smaller and smaller. At the same time, the increasement of the overhead burden also limits the gain of adding more hypothesis. Also the limitation of combining MHMC techniques along with the vector burden are discussed.
This paper presents a scheme that apply the pixel-wise masking technique used in image watermarking into video sequence. The proposed algorithm deploys the video watermarking in the redundant discrete wavelet transform (RDWT) domain. The advantages of using an overcompleted wavelet transform instead of the traditional critically subsampled discrete wavelet transform (DWT) are discussed. The redundancy in the transform domain facilitates a better detection
of the video texture characteristics in a video sequence. Thus leads to an efficient watermark casting scheme, in which more strength of watermark can be embedded into the video sequence, but still not perceivable. Different methods of using RDWT or DWT coefficients to add the watermarking are compared. Experimental results show that RDWT domain video watermarking offers greater robustness than DWT