@article{kiko2025,

title = {Parallelized SLAM: Enhancing Mapping and Localization through Concurrent Processing},

author = {Francisco Romero-Ramirez and Miguel Cazorla and Manuel Marin-Jimenez and Rafael Medina-Carnicer and Rafael Munyoz-Salinas},

year  = {2025},

date = {2025-01-01},

journal = {Sensors},

volume = {Aceptado},

number = {1},

keywords = {AI, computer vision, mapping, robotics, SLAM},

pubstate = {published},

tppubtype = {article}

}

@article{s25010021,

title = {Reliable Disparity Estimation Using Multiocular Vision with Adjustable Baseline},

author = {Victor H. Diaz-Ramirez and Martin Gonzalez-Ruiz and Rigoberto Juarez-Salazar and Miguel Cazorla},

url = {https://www.mdpi.com/1424-8220/25/1/21},

doi = {10.3390/s25010021 keywords = stereo, computer vision, multiocular, AI},

issn = {1424-8220},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Sensors},

volume = {25},

number = {1},

abstract = {Accurate estimation of three-dimensional (3D) information from captured images is essential in numerous computer vision applications. Although binocular stereo vision has been extensively investigated for this task, its reliability is conditioned by the baseline between cameras. A larger baseline improves the resolution of disparity estimation but increases the probability of matching errors. This research presents a reliable method for disparity estimation through progressive baseline increases in multiocular vision. First, a robust rectification method for multiocular images is introduced, satisfying epipolar constraints and minimizing induced distortion. This method can improve rectification error by 25% for binocular images and 80% for multiocular images compared to well-known existing methods. Next, a dense disparity map is estimated by stereo matching from the rectified images with the shortest baseline. Afterwards, the disparity map for the subsequent images with an extended baseline is estimated within a short optimized interval, minimizing the probability of matching errors and further error propagation. This process is iterated until the disparity map for the images with the longest baseline is obtained. The proposed method increases disparity estimation accuracy by 20% for multiocular images compared to a similar existing method. The proposed approach enables accurate scene characterization and spatial point computation from disparity maps with improved resolution. The effectiveness of the proposed method is verified through exhaustive evaluations using well-known multiocular image datasets and physical scenes, achieving superior performance over similar existing methods in terms of objective measures.},

key = {computer vision, deep learning, AI},

keywords = {AI, computer vision, deep learning},

pubstate = {published},

tppubtype = {article}

}

@article{app14209414,

title = {VFLD: Voxelized Fractal Local Descriptor},

author = {Francisco Gomez-Donoso and Felix Escalona and Florian Dargere and Miguel Cazorla},

url = {https://www.mdpi.com/2076-3417/14/20/9414},

issn = {2076-3417},

year  = {2024},

date = {2024-01-01},

journal = {Applied Sciences},

volume = {14},

number = {20},

abstract = {A variety of methods for 3D object recognition and registration based on a deep learning pipeline have recently emerged. Nonetheless, these methods require large amounts of data that are not easy to obtain, sometimes rendering them virtually useless in real-life scenarios due to a lack of generalization capabilities. To counter this, we propose a novel local descriptor that takes advantage of the fractal dimension. For each 3D point, we create a descriptor by computing the fractal dimension of the neighbors at different radii. Our redmethod has many benefits, such as being agnostic to the sensor of choice and noise, up to a level, and having few parameters to tinker with. Furthermore, it requires no training and does not rely on semantic information. We test our descriptor using well-known datasets and it largely outperforms Fast Point Feature Histogram, which is the state-of-the-art descriptor for 3D data. We also apply our descriptor to a registration pipeline and achieve accurate three-dimensional representations of the scenes, which are captured with a commercial sensor.},

keywords = {3D processing, AI, computer vision, fractal},

pubstate = {published},

tppubtype = {article}

}

@article{dominguez2024holograms,

title = {Holograms for seamless integration of remote students in the classroom},

author = {Bessie Dominguez-Dager and Francisco Gomez-Donoso and Rosabel Roig-Vila and Felix Escalona and Miguel Cazorla},

year  = {2024},

date = {2024-01-01},

journal = {Virtual Reality},

volume = {28},

number = {1},

pages = {24},

publisher = {Springer},

keywords = {AI, augmented and virtual reality, computer vision, Education},

pubstate = {published},

tppubtype = {article}

}

@article{gomezDonoso2022c,

title = {Three-Dimensional Reconstruction Using SFM for Actual Pedestrian Classification},

author = {Francisco Gomez-Donoso and Julio Castano and Felix Escalona and Miguel Cazorla},

year  = {2023},

date = {2023-01-01},

journal = {Expert Sytems with Applications},

volume = {213},

keywords = {3D reconstruction, AI, computer vision, deep learning, pedestrian classification, structure from motion},

pubstate = {published},

tppubtype = {article}

}

@article{9701312,

title = {3DSliceLeNet: Recognizing 3D Objects Using a Slice-Representation},

author = {Francisco Gomez-Donoso and Felix Escalona and Sergio Orts-Escolano and Alberto Garcia-Garcia and Jose Garcia-Rodriguez and Miguel Cazorla},

doi = {10.1109/ACCESS.2022.3148387},

year  = {2022},

date = {2022-01-01},

journal = {IEEE Access},

volume = {10},

pages = {15378-15392},

keywords = {3D object recognition, AI, computer vision, deep learning},

pubstate = {published},

tppubtype = {article}

}

@article{donoso2022b,

title = {UrOAC: Urban Objects in Any-light Conditions},

author = {Francisco Gomez-Donoso and Marcos Moreno-Martineza and Miguel Cazorla},

year  = {2022},

date = {2022-01-01},

journal = {Data in brief},

volume = {Aceptado},

keywords = {AI, any-light conditions, computer vision, dataset, urban objects},

pubstate = {published},

tppubtype = {article}

}

@article{Fusco2016,

title = {Computer vision profiling of neurite outgrowth dynamics reveals spatiotemporal modularity of Rho GTPase signaling},

author = {L. Fusco and R. Lefort and K. Smith and F. Benmansour and German Gonzalez-Serrano and C. Barillari and B. Rinn and F. Fleuret and P. Fua and O. Pertz},

doi = {10.1083/jcb.201506018},

issn = {15408140},

year  = {2016},

date = {2016-01-01},

journal = {Journal of Cell Biology},

volume = {212},

issue = {1},

pages = {91-111},

abstract = {...},

keywords = {AI, computer vision, morphodynamics, neurite outgrowth, Rho GTPase},

pubstate = {published},

tppubtype = {article}

}

@article{CAE:CAE21594,

title = {JavaVis: An integrated computer vision library for teaching computer vision},

author = {Miguel Cazorla and Diego Viejo},

url = {JavaVis: An integrated computer vision library for teaching computer vision},

doi = {10.1002/cae.21594},

issn = {1099-0542},

year  = {2015},

date = {2015-03-14},

journal = {Computer Applications in Engineering Education},

volume = {23},

issue = {2},

keywords = {AI, computer vision},

pubstate = {published},

tppubtype = {article}

}

@article{1197837,

title = {Two Bayesian methods for junction classification},

author = {Miguel Cazorla and F Escolano},

doi = {10.1109/TIP.2002.806242},

issn = {1057-7149},

year  = {2003},

date = {2003-01-01},

urldate = {2003-01-01},

journal = {Image Processing, IEEE Transactions on},

volume = {12},

number = {3},

pages = {317-327},

keywords = {AI, computer vision, edge detection, junction classification},

pubstate = {published},

tppubtype = {article}

}